
Ask HN: Physicists of HN, what are you working on these days? - sachin18590
Of late, except for few headline-friendly fields (colliders, quantum computing, gravitational waves and astrophysics in general), I don&#x27;t get to see&#x2F;relate with a lot of activities in Physics. Also I have noticed a growing trend of physicists becoming data scientists post phD. Although I understand the money factor, are there any other reasons for this as well?
======
eightorbit
Just so you don't get the impression every physicist is having a fabulous
time, here's me:

I have a bachelors in physics. I went for a phd in biology but had to bail to
work to support family, plus I can't seem to come up with original ideas.

I now have a job with physicist as part of the title, but what I really do is
try to read bad handwriting from records from nuclear weapons plants. It's a
dull job a monkey could do but it pays the bills. For years I worked as a
computer guy for an academic department and that was fun and I'm trying to get
back into it but nothing yet. I teach math at a community college for fun too.

I have great kids, so all and all I'm happy about things, but I am sad the
physics thing never panned out. As an undergrad I was excited about chaos and
nonlinear dynamics. Still read math texts for fun and play with Haskell.

~~~
kriro
"""try to read bad handwriting from records from nuclear weapons plants. It's
a dull job a monkey could do but it pays the bills."""

+

"""For years I worked as a computer guy for an academic department and that
was fun and I'm trying to get back into it"""

Sounds like you could enjoy automating your work. Learn a bit of Python and
some Machine Learning/Deep Learning and digitize the handwriting and build a
little program that reads the stuff for you. The default example for reading
handwritten digits is called MNIST if you want to read further. I'd suggest
fast.ai and watching the first couple of lessons. That should get you started
to play around with this. You don't have to tell anyone that you do this (I
probably wouldn't) but hey at least it might be a nice way to do a little less
boring stuff and ease into a bit of programmin/data science?

~~~
superhuzza
I would be very, very hesitant to to this.

First of all, you definitely don't want any chance of incorrect recognition of
handwriting coming from a _nuclear weapons plant_.

Secondly, who knows what policies he might violate by using some unauthorized,
untested software (whether OP is the author or not) with potentially sensitive
information.

~~~
ratsimihah

        First of all, you definitely don't want any chance of incorrect recognition of handwriting coming from a nuclear weapons plant.
    

Loving this, this sounds like the plot of a bad sci-fi movie.

~~~
debatem1
It reminds me of the start of "Brazil", bugs and all.

------
djaque
(Current PhD Student)

Although the case for building new and more powerful hadron accelerators
doesn't look good, accelerator physics is flourishing with other types of
machines.

Particularly interesting to me is ultrafast electron diffraction (UED)[1,2].
UED is cool because you can create atomic resolution movies with speeds that
can (in the near future) resolve chemical reactions as they occur. (eg.
imagine being able to see a protein change conformations in a biological
reaction)

This application is limited by the number of electrons we can stick in a given
volume and get traveling in the same direction. The only way to improve this
is by increasing the electric field in your electron gun or by choosing good
materials for your photocathode. [3]

My research is on the second route and I'm currently building a measurement
system that will allow us to test several theories related to how we choose
these materials. Improvement in this domain is important and could open up a
huge amount of research, but unfortunately doesn't get the kind of publicity
that the big projects do.

[1] [https://lcls.slac.stanford.edu/instruments/mev-
ued](https://lcls.slac.stanford.edu/instruments/mev-ued)

[2] Dwyer, J. R., Hebeisen, C. T., Ernstorfer, R., Harb, M., Deyirmenjian, V.
B., Jordan, R. E., & Dwayne Miller, R. J. (2006). Femtosecond electron
diffraction:‘making the molecular movie’. Philosophical Transactions of the
Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1840),
741-778.

[3] Rao, T., & Dowell, D. H. (2014). An engineering guide to photoinjectors.
arXiv preprint arXiv:1403.7539.

~~~
PopeDotNinja
> Although the case for building new and more powerful hadron accelerators
> doesn't look good, accelerator physics is flourishing with other types of
> machines.

Relevant "I saw this on YouTube" video:

\- "Should we build a bigger particle collider? - Sixty Symbols"

\- [https://youtu.be/-cD66O01E4E](https://youtu.be/-cD66O01E4E)

\- TL;DR: the LHC has only found one new particle, and it was looking for it.
Before spending 30 years and 10-20 billion pounds on a 4x bigger collider,
maybe we should wait until we have an idea of what we'd be looking for, as
it's not clear what the larger collider would be looking for. The downside of
not building a new collider soon is that the people who know how to build a
collider now won't sit around waiting until we decide to build one, so
starting from scratch in the future will presumably take longer & be more
expensive.

~~~
djaque
Yeah, and another recent piece of news is that the design committee for the
international linear collider (ILC) which was to be built in Japan concluded
that there wasn't a physics justification for it. Although a final decision
needs to be made from the Japanese government, it looks like the ILC is dead
leaving the future circular collider (FCC) as the only large new design.
(Edit: it looks like Japan has decided not to build the ILC [1])

However, I think there is a lot of interesting physics for accelerators beyond
colliders. For instance, the linac coherent light source (LCLS) is a huge
x-ray laser out in California [2]. They are in the middle of a big upgrade and
employ a large number of accelerator physicists. There is also still a huge
amount we need to learn about how free electron lasers work and how we can
improve them.

Another big topic of research is energy recovery linacs (ERLs). We are just on
the cusp of being able to implement a technology that could save something
like 90% of the wall plug power of current accelerators. They draw a huge
amount of power, so the benefit of this saving is pretty clear. The first
machine to demonstrate this new technology will come online this summer
(hopefully). [3]

[1] [https://physicsworld.com/a/disappointment-as-japan-fails-
to-...](https://physicsworld.com/a/disappointment-as-japan-fails-to-commit-to-
hosting-the-international-linear-collider/)

[2] [https://lcls.slac.stanford.edu/](https://lcls.slac.stanford.edu/)

[3]
[https://www.classe.cornell.edu/Research/ERL/CBETA.html](https://www.classe.cornell.edu/Research/ERL/CBETA.html)

------
ISL
Physicist, working as a physicist (PhD 2015). Precision tests of gravity,
primarily. We test the equivalence principle, Newton's inverse square law,
hunt for exotic forms of dark matter, and build/invent cutting-edge sensors
for LIGO.

I have begun looking about at jobs outside of academia in order to decide
whether to stay or go. Data science is one of the easiest transitions a
physicist with data-analysis can make, which I think explains the prevalence
of physicists in that role. We have the training in both the techniques and a
sober assessment of uncertainties, which makes us desirable.

So far, in my search for outside employment, I haven't found anything that
draws me as much as my present work, but if you're in the Seattle area and
looking for an experimental physicist with a broad range of experience, please
get in touch.

~~~
BurningFrog
> _We test [...] Newton 's inverse square law_

Imagine taking Newton down in the Replication Crisis Wars!

~~~
joe_the_user
Well, I think that's a problem with calling this situation "The Replication
Crisis". The basic laws of physics have been verified to an extraordinary
degree of accuracy on the that people deal with them daily and even at the
ordinary atomic level - if anything, physics has the opposite crisis where
it's theories are not fully self-consistent on every hypothetical condition
but where in every condition we can produce, one or another theories works
with apparently perfect precision.

"The Replication Crisis" has basically involved rather different fields -
psychology and human biology/medicine most often. The crisis could be called
"human and animal testing crisis" imo.

[https://en.wikipedia.org/wiki/Replication_crisis](https://en.wikipedia.org/wiki/Replication_crisis)

------
musgravepeter
(PhD, 1996 in general relativity)

Embedded graphics drivers for real-time systems.

I keep the physics part of my brain alive by developing physics based Unity
assets (nbodyphysics.com) and supporting a package for GR on github
(grtensor).

I still buy WAY too many physics books. Current aspiration is to work through
"Modern Classical Physics" Thorne/Blandford.

~~~
campallison
I bought Modern Classical Physics last summer as a birthday gift to myself and
I am also slowly going through it.

~~~
hyperjeff
Ex-PhD student in general relativity here, also slowly going thru that book.
If anyone wants to start a reading (slack?) group together, I’d be interested.
I have no one to talk to about this stuff IRL.

~~~
vijayshankarv
I would love to join a reading group (on slack or another messaging platform)
on this book.

------
da-bacon
I currently work on the software that runs Google's quantum computers. To
paraphrase the Talking Heads: How did I get there?

PhD 2001 in physics, working on quantum computation. Postdocs at Caltech and
Santa Fe Institute, then landed a research faculty position at the University
of Washington. Yeah, raise your own funds! Jumped ship in 2011 (burnout,
quality of life, university not caring about quantum computing) and went off
to become a "real" software engineer at Google. Worked on ads (as one does),
then helped build Google Domains, then worked on distributed privacy
preserving machine learning. About two years ago, my background in quantum
computing caught back up to me, and now I run the team that builds software
for Google's quantum computers.

People ask how to get into quantum computing if you are a software engineer. I
will say that you really need to spend some deep time in quantum computing,
either a masters or a PhD or some very very serious self study. There are
certainly parts of writing software for quantum computing that don't require
that, but if you really want an expansive career working in quantum computers
you'll want to have a deep background.

~~~
MLWithPhil
Very cool stuff. To what extent would you recommend people study the
underlying physics of QM, vs. the more domain specific content of quantum
computing?

~~~
da-bacon
I'd say start with the quantum computing specific stuff and then add the
physics. It will make some of the quantum physics stuff easier, I think.

------
jauntbox
I got a PhD in 2013 (theoretical/computational astrophysics), did a postdoc
for two years, and was a professor for one year. I’m now a data scientist at a
large tech company in the Bay Area working on a machine learning platform.

I enjoyed grad school for the most part, and was really into teaching. By the
time I got a professor job (a visiting one, not tenure-track), I was getting
anxious about the long-term job prospects and it was getting harder and harder
to justify the workload (teaching, grants, advising students, etc) given the
relatively poor job security and pay. I felt if I was going to switch careers,
I should do it soon since it’s not going to get any easier.

By this point (~3 yrs ago) I had several physics/astro friends who had become
data scientists or similar jobs in the tech industry. Some had done programs
like Insight and some got jobs on their own. Everyone I talked to seemed happy
with their decision to switch careers. I ended up doing Insight and getting a
job quickly after and am glad I did. The variety of the work, amount of
collaboration (more), and new things to learn is still keeping me interested.
I was also surprised at how many opportunities there are to give talks and
seminars in the industry, which helps scratch the teaching itch.

~~~
Falcorian
Similar story with me: decided I didn't want to join the long-term job hunt
and went to Insight (4 years ago).

I've been very happy doing data science ever since. It's great to work more
collaboratively, ship more quickly, and learn from great engineers.

------
loarake
Just defended my PhD thesis in medical physics. Worked on radiation therapy
treatment planning, which combines optimisation theory with the physics of
Monte Carlo particle transport engines (and more macro energy deposition
modeling as well) to simulate millions of different radiation dose
distributions in patients and figure out which combination will lead to the
right outcome based on what the radiation oncologist prescribes.

People in my field are fairly fortunate as there is a career track as a
clinical medical physicist that is highly paid and pretty low stress, so most
people end up going there. The work consists of maintaining and calibrating
the radiation therapy machines, along with implementing new technologies in
the clinic, and fixing problems that don't fall within the job description of
the radiation therapists. Like what to do when a radioactive seed falls on the
floor instead of going inside the patient where it's supposed to go. There's
also a separate track as an imaging physicist where you maintain and QA the
diagnostic imaging machines.

I'm personally doing a postdoc at the junction between optimisation, machine
learning and radiation therapy. Just starting out though. Basically just
extending my PhD work to automate the treatment planning process and remove
the variability in treatment plan quality due to the level of experience of
the people making the plans.

~~~
blevin
Sounds fascinating. What coordinate systems are used for treatment planning?
Given how much bodies can change over time, and the difficulty of re-achieving
a specific pose, I'm curious if there are interesting ways to correlate
measurements over time. Certainly, medical training involves learning lots of
prepositional anatomy words like "antecubital" but is there anything more
precise, a GPS system for bodies? This seems very challenging for e.g. the
gastrointestinal tract -- but I could imagine something using lots of relative
reference points, the way I assume surgeons orient themselves.

~~~
loarake
It's much more primitive than you think. Dose distributions are simulated
based on a CT/MRI that was acquired before treatment (treatment often lasts
weeks). Only minor corrections are made when anatomy changes during the course
of treatment, even though the patient is often losing tons of weight due to
chemo, etc. There are quite a few tools that help with patient positioning,
like vac-lok bags or literally molding a mask and drilling it down on the
treatment couch (an example is shown here:
[https://newsnetwork.mayoclinic.org/discussion/new-
radiothera...](https://newsnetwork.mayoclinic.org/discussion/new-radiotherapy-
treatment-for-brain-cancer-offers-superior-preservation-of-cognitive-function-
mayo-researchers-say/)).

Motion during treatment can be tracked with cameras or IR sensors or
subcutaneous probes but that doesn't tell you about internal organs moving.
The topic of deformable registration, where you find a non-rigid mapping
between initial imaging conditions and the current ones, is still a topic of
active research. Adaptive planning, where you actively change the treatment
plan every N sessions based on the most up to date information, is also
actively researched / implemented in some good research centers.

For treatment planning you just use a standard Cartesian grid, or a "beam's
eye view" coordinate system that's aligned with the radiation beam axis as it
rotates around the patient.

~~~
blevin
Makes sense; thanks. I'm out of my depth but it seems neurosurgery may just
have it easier here, being able to fix a rigid stereotaxy head frame and
fiducial markers across both imaging and therapy. Not to mention less tissue
deformation enabling a gamma knife intersection-of-beams approach (i.e. ~200
collimated, mm-wide gamma sources).

Not to be glib but on behalf of the thousands of people going into a
radiotherapy clinic today for treatment, thanks for working to improve these
techniques.

------
dphidt
I am a physicist working as a physicist! My field of experimental particle
physics, at least, is very hacker-ey: we develop instrumentation hardware,
low-level data acquisition software, database and web apps for experiment
operations and monitoring, and the like, beyond the data analysis. Of course,
there are many industry opportunities for people with this kind of experience.
In addition to the issues pointed out in other comments (small number of
academic positions, salary differences), I think there are two cultural
factors that are helping people transition out of academia: an increasing
awareness on the part of advisors and institutions that students need more
broadly marketable experience, and a corresponding decrease in the stigma of
leaving academia. As one example, I routinely see notices distributed for
Insight data science programs within our community.

~~~
pontifier
Would you be interested in talking to me about a new miniature particle
collider device I've come up with? I haven't yet built a prototype, but I need
to ensure that I gather the correct data to determine if it functions the way
I believe it will.

------
PaulHoule
When I got my PhD (1999) the American Physical Society said that you had a 2%
chance of getting a permanent job in the field with a PhD. At that point you
are not being judged on your merits but on your connections, ability to
navigate politics, etc. (The job is way too valuable compared to the value you
can give to it.)

I saw a postdoc who is now rather well known struggling with anxiety over his
career even though he had written half a book and done a lot of great work.
When we were both at Cornell I'd come to the conclusion that many papers
involving "power law" distributions were bogus because nobody knew how to test
for them with any rigor. It was years later, after he had tenure, that he
published something about it _in a statistics journal_.

Seeing that made me run for the exit after my first year as a postdoc.

~~~
mjburgess
> I'd come to the conclusion that many papers involving "power law"
> distributions were bogus

> after he had tenure, that he published something about it in a statistics
> journal

If I'm reading you right, you're saying he struggled for awhile doing bogus
things only to question those things outside the relevant field?

(Also, can you say more about power law papers..?)

~~~
PaulHoule
I wrote one (experimental) bogus paper. He wrote quite a few papers, I'm not
sure if any of them used the experimental non-technique that was common at the
time.

So there is a theory based on the renormalization group that can explain many
(but not all) "fractal" phenomena that are observed.

I was part of the false paradigm of "bin up a probability distribution", "plot
it up on log-log paper", and "draw a straight line."

Sometimes when you do that you will get an answer that has something to do
with reality but it is not a valid answer to the problems of: "is a power law
distribution a good model of this phenomenon", "what is a good estimate of the
exponent", "are these two power law charts drawn from the same distribution"
not to mention how to handle the problems that turn up at the highly frequent
and highly rare ends of the distribution.

The root cause is this attitude

[http://thinkexist.com/quotation/if_your_experiment_needs_sta...](http://thinkexist.com/quotation/if_your_experiment_needs_statistics-
you_ought_to/338030.html)

and an academic system where power is too concentrated, where people who write
review articles do the most good but get the least career advancement, etc.

------
sandgiant
I have PhD in physics. Now working in IT startup team of five coders from
various academic backgrounds. Been doing everything from app development to
web, admin/ops, some elixir, lots of python. Currently working on a few
machine learning projects, collecting data, making features, scaling
infrastructure and so on.

Salary is decent, everything is moving a lot faster than at uni. Wouldn't say
I miss academia as such, but I definitely miss working on actual fundamental
physics problems.

I might go back some day to apply my new knowledge of operational data
science. There's definitely a need for updated methods, especially in data
heavy fields such as astrophysics.

Edit: To answer your question about what's better outside academia - I'd say
for me it's the tighter collaboration with colleagues, better project
management, clearer goals, more diverse team (in terms of educational
background / role in company), and last but not least job security - I can
live where I want, not where the next postdoc happens to be.

------
jberger1613
PhD in 2012, Theoretical high energy particle physics.

Now I work optimizing fantasy sports teams and building websites that display
betting lines. Don't even like sports and I have no idea what anyone in the
office is talking about, but they are paying a lot for me to put buttons on
their website I guess. Leaves me a bunch of room for my hobbies. Offered to do
some actual math, maybe personalization algos or some AI stuff. Backtesting
automation to determine if our data is at all valid that we're selling?

Really just need these buttons on the site is all.

~~~
pontifier
Do you have any interest in talking with me about a fusion reactor I am
working on? I'm sure you would have some valuable insights.

------
sprash
Nuclear physicist now developing options strategies and corresponding
specialized financial software for a hedge fund. I'd rather go back and work
at the particle accelerator but there is zero chance for a permanent position
as a researcher for a regular dude like me.

~~~
bluedevilzn
I bet hedge fund pays significantly more too, no?

~~~
SiempreViernes
Unless they accept you working less hours there's not that much joy to be
derived from more money for boring tasks.

~~~
quickthrower2
You can do a 4 hour average week though: Just do a year of 60 hour weeks then
quit and live off that money for 14 years.

------
vijayshankarv
Condensed matter physics PhD (2016) now working in the software industry as a
data scientist.

Focused on modelling/simulating materials during my thesis and realized that I
loved the software aspect of things and did not love working on the same
problem for many months at a time.

As others mentioned, transitioning to data science is not that hard if you
have a physics background and there are many interesting problems to solve in
the area.Most of my graduate student peers are also in data science/ML and
related areas (software/finance).

Although money was a contributing factor, the main reasons to leave academia
were being able to live in a city that I liked and where my partner could also
find a position.

Did not look back on physics at all for the first couple of years post-PhD,
but missing it quite a bit nowadays. End up buying a lot of Physics books
every year, although don't get through many of them. Latest purchase was
Exercises for the Feynman lectures.

~~~
hpcjoe
The two body problem (2 professionals in academia finding jobs close to each
other, for some value of close that is reasonable for daily life) is famous in
science in general. It is, unless one of you has a Nobel Prize, unsolvable
without one person making a change of career.

~~~
whyever
> It is, unless one of you has a Nobel Prize, unsolvable without one person
> making a change of career.

This is not really true, I know a few people who managed.

------
dheera
I was a dual physics/EE major at MIT for my undergrad. For my PhD at MIT I was
in the EE department working on quantum and single-photon imaging.

I now work on mostly computer vision, machine learning, and robotics. Robotics
was a hobby of mine since high school, and is now my work.

I'm still passionate about physics and physics research, but I'm not happy
with the academic system of today, which encourages pursuing a lot of low-
hanging fruit just to publish and get tenure, instead of going after high-
risk, potentially-groundbreaking, but likely to fail topics.

While doing my PhD one of the biggest questions I kept having is why the
primary goal the system set for me is "to graduate" and not "to advance
science". On several occasions faculty told me to not try something "because I
would never graduate" if I went down those paths.

~~~
mensetmanusman
MIT alum here, found the materials science PhD route very rewarding (from a
physics undergrad). You may have done research with K. Berggren it sounds like
:)

I jumped into industry hoping to improve how the world makes things on a very
large scale. Current project has potential to change how a ubiquitous product
is made; it would be the first major manufacturing change in 80 years!

------
physics137
I'll leave this here, since many comments are about the transition from
physics to data science.

"For now, however, in hard-core physical science at least, there is little
evidence of any major BD-driven breakthroughs, at least not in fields where
insight and understanding rather than zerosales resistance is the prime
target: physics and chemistry do not succumb readily to the seduction of
BD/ML/AI. It is extremely rare for specialists in these domains to simply go
out and collect vast quantities of data, bereft of any guiding theory as to
why it should be done. There are some exceptions, perhaps the most intriguing
of which is astronomy, where sky scanning telescopes scrape up vast quantities
of data for which machine learning has proved to be a powerful way of both
processing it and suggesting interpretations of recorded measurements. In
subjects where the level of theoretical understanding is deep, it is deemed
aberrant to ignore it all and resort to collecting data in a blind manner.
Yet, this is precisely what is advocated in the less theoretically grounded
disciplines of biology and medicine, let alone social sciences and economics.
The oft-repeated mantra of the life sciences, as the pursuit of ‘hypothesis
driven research’, has been cast aside in favour of large data collection
activities [7]. And, if the best minds are employed in large corporations to
work out how to persuade people to click on online advertisements instead of
cracking hard-core science problems, not much can be expected to change in the
years to come. An even more delicate story goes for social sciences and
certainly for business, where the burgeoning growth of BD, more often than not
fuelled by bombastic claims, is a compelling fact, with job offers towering
over the job market to anastonishing extent. But, as we hope we have made
clear in this essay, BD is by no means the panacea its extreme aficionados
want to portray to us and, most importantly, to funding agencies. It is
neither Archimedes’ fulcrum, nor the end of insight."

[https://royalsocietypublishing.org/doi/full/10.1098/rsta.201...](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2018.0145)

~~~
hpcjoe
The lack of a guiding theory has been something I've talked about with regard
to big data and ML in general. This said, if you look closely at the math
behind ML, it looks, not to surprisingly, like statistical mechanics.

So if we take some of the thought processes behind Stat Mech (or S&M as we
used to call it in grad school), and you kinda squint your eyes hard to blur
the less robust discussions you read about, you get the sense that ML is more
about the "thermodynamics of information" than anything else.

I find this intriguing and definitely want to spend more time on this stuff.

~~~
madhadron
It also looks _exactly_ like decision theory from statistics done in high
dimensional spaces. Mostly because it is.

------
cosmic_quanta
Condensed matter physics PhD student here, working on ultrafast electron
diffraction.

The field of ultrafast science brought forward by the advances in ultrafast
laser technology (Nobel 2018!) is exploding. On the largest scales, the Linac
Coherent Light Source and similar machines are generating a lot of buzz. It
hasn't yet reached the public recognition of the LHC, for example, but it's
only a matter of years.

A colleague of mine was just hired as a data scientist at a major tech
company. Based on the interview questions I heard from him, it seems that the
data science field is a natural extension of the kind of data analysis we
perform daily.

------
Maro
I have an Msc in Physics. I got it after my CS Msc, because I got really
hooked on physics after reading tons of Feynman stuff (FLoP, etc). I've been
programming since age 7 (C64), so I'm a programmer who got a Physics degree,
not the other way around.

I worked various SWE jobs initially, mostly C++.

Then I started a Phd in Physics, but didn't finish, bc I did a startup [1].
The startup failed.

Since then I've been working in various data/science roles at companies
(Prezi, Facebook, now Fetchr).

Data Science is a perfect fit for me (CS+Physics), there's nothing around it
that I can't do, from setting up stuff on AWS, building dashboards, A/B
testing, getting data out with SQL, doing ML with SKL or Pytorch, defining
metrics and setting high-level company goals, explaining stuff to the CxOs.

I'm really good at it, I have very high impact, get paid good money, make my
own rules (mastery, autonomy, etc).

Overall I got lucky because data/science exploded, and it just so happened
that my interests/background/experience were a perfect fit.

I got doubly lucky bc now Deep Learning is exploding [2], which is a perfect
playground for people like me (play around with models, metrics, training,
loss functions, etc.)

I still buy lots of Physics books and sometimes read papers, but I'm trying to
quit that, bc it's mostly pointless.

[1]
[https://github.com/scalien/scaliendb](https://github.com/scalien/scaliendb)

[2]
[https://news.ycombinator.com/item?id=19499515](https://news.ycombinator.com/item?id=19499515)

------
joeberon
I’m working on reducing the quantum noise of gravitational wave detectors as
well as work in quantum metrology. Measurements on the quantum scale using
light are still relatively unexplored and there is a lot of work to do.

I just sent my first paper for publish, a new design for a gravitational wave
detector with reduced quantum noise

~~~
bawana
wow. interesting.so we might be able to detect gravity waves without kilometer
long lasers?

------
fsloth
I got my masters, then pursued a PhD for 6 months after which an opening in
computer graphics came up in the industry. I abandoned my PHd and started a
career as software engineer.

The reason was I sucked in physics, did not find my PhD position motivating
and loved computer graphics.

13 years later I'm a valued technical contributor in a team in an ISV creating
a valuable global software packages in the CAD field.

Middle class income, could probably make lot more in US with my skillset but
family situation really is not awesome for expatriation.

I'm doing OK.

With physics skillset you can pretty much make your career what you want it to
be. You just need a proactive attitude to read on other fields. You need to
understand the other guys mindset so you understand the overall game going on.
It's usually not nefarious (although it can be), but most of the time the
rules that are used are not the vocalized ones. In this empirical physics is a
wonderfull philosophical background. Organizations have certain dynamic rules
which half of the people are not aware of. You don't need to "play the game",
but you need to understand the rules so that when the wind blows into the
direction you want to go into you can grab the opportunity.

I had a pretty good idea where I wanted to be 13 years ago (R&D in a position
that values quality over quantity and speed with a great team) and that's
pretty much where I am now.

You need to know where you want to go, and go there. No one will guide your
path. Physicists are an outlier but that math and mindset is really an asset.
Just don't get stuck in fixing bugs in some legacy monstrosity, that is
absolutely soul crushing. But such a position can function as a stepping stone
if you are operating in the industry you want to work in.

~~~
tobmlt
Fixing bugs in a legacy monstrosity checking in! Updating my resume this week.

What's an ISV?

~~~
fsloth
That's what I was saying - you need to understand the game. Part of this is
knowing the core business terminology in you field :)

ISV - independent software vendor. An entity having an ownership of a software
product, usually developing the software as well.

The other entity this is often compared against is the ESP - external service
provider. I.e. consultants.

I spent years with legacy monstrosity and it does develop important skills. If
the organization is otherwise ok it might not be that bad - but generally
production software is absolutely horrible. The thing is legacy maintenance is
important, and there's quite nothing like it that will teach you about the
lifecycle requirements of software development. But I find it much more
fullfilling to maintain and develop software that is alive and well and
actively kept out of the 'legacy' label.

Not all old software is 'legacy'. One definition is that does it have tests.
I've found an equally good definition of 'you are not afraid to modify it' and
'you can understand what changes in one place do elsewhere'.

So to be clear, I was not saying "anyhing but greenfield development sucks".

------
JWKennington
I wanted to provide a different answer from many of those "glad I left"
answers here. I sympathize with those "keep the physics brain alive" goals of
other commenters.

Physics undergrad (2015) with thesis in nonlinear dynamics, but have done
research in astro. Always wanted to do GR-related research.

Landed an awesome job in quantitative finance where I write computational code
(distributed computation) for various purposes (researching markets, analyzing
risk, etc.). Job is interesting - but not "capital I" Interesting in the same
way that physics was. I guess that is part of the trade-off.

I'm considering going back to academia - especially in light of the G-wave
phenomena finally having sizeable datasets to analyse in recent years - its
getting increasingly difficult to keep my studies of physics/math at "hobby"
level.

Interested to hear if anyone else out there has "gone back".

------
gotfork
Building better quantum computers.

I did a PhD in condensed matter, finishing in late 2014. I'm now working as a
SWE at a quantum computing startup, focusing on internal manufacturing and
test data. My graduate experience is very useful for this position, even
though I'm not doing much physics directly.

I absolutely don't regret getting a PhD -- I went to a well-run program, had a
great advisor, and met tons of awesome people. I also don't regret leaving
academia. I enjoy the "working on hard problems" bit a lot more than the
"unlocking secrets of the universe" part.

------
jessriedel
> Also I have noticed a growing trend of physicists becoming data scientists
> post phD.

The number of basic research jobs in physics is a pretty fixed supply, since
it's determined largely by public funding, and there are always many more
applicants than jobs. Therefore, any trends you've noticed about physicists
leaving the field for other jobs is much more reflective of the number of
students entering the field (always rising) who eventually must leave, and the
extent to which they publicly discuss their experiences. If anything, hearing
about more people leaving physics would be a _positive_ indicator for the
desirability of the field, since it just means more people tried and failed to
obtain a slot. (As it happens, I think fundamental physicists research is a
pretty diseased field, but a shortfall in researchers due to PhDs being drawn
away to greener pastures is not a symptom.)

Note that applied physics is different, since there is an actual market in
researchers for industrial R&D; a shortfall in jobs really could reflect a
failing field. My vague impression is rather that applied physics is booming,
but know very little about that.

And just to be clear on the numbers: If, over a career, every research
professor mentors N grad students, and the number of research professor jobs
grows slowly (as it has since the explosive growth in prof jobs that peaked in
the '60s has waned), then the chance of any PhD becoming a professor must be
about 1/N. If you look at the numbers, that chance is a few percent, which
agrees with the typical professor advising ~20 students over a career. That
means ~95% of PhD are going to be leaving to do something else.

Anyways, for me personally: I have a PhD in quantum information and am
currently searching for a mathematical definition of branches in a many-body
wavefunction. This would potentially lead to large computational speed-ups in
numerical simulations of out-of-equilibrium systems. I'm in my 7th year of
postdoc-ing which...is not ideal.

------
cozzyd
I'm a postdoc (PhD 2015) working on radio detection of ultra-high-energy
neutrinos. I spend most of my time writing software and doing data analysis,
but I also do field work (including in Antarctica and Greenland!) and a
million other things. It's a whole lot of fun except for the general
uncertainty about what I'll be doing in the future part.

~~~
smueller1234
I used to have that uncertainty (was in Auger as a PhD student, so a similar
field), but assuming it's something you think you might enjoy doing, going
from writing analysis software in astrophysics to doing that as a primary
profession is not very hard. It does likely come with a reset of expectations,
going back to primarily learning instead of teaching for a little while.

Happy to expand on my experience if that would be helpful.

~~~
frob
The Auger detector is one of the coolest detectors I've seen. I had to do a
report on it in grad school. My favorite factoid is that it is the size of
Rhode Island. Also, at the time, it had seen particles in the 10s of EeV
range, which is also known as a Joule. The same kinetic energy as a 2 kg
object traveling ~1 m/s. In ONE SINGLE PARTICLE.

I love physics.

~~~
smueller1234
Anecdote time!

Back in 2007, I was writing my diploma thesis (think master's just twice as
long, German system prior to bachelor/master) at Auger. The collaboration has
a rule that for the first year in, you don't go into the author list, but stay
on it for a year after you leave. That optimizes for having the actual
contributors on papers. Towards the end of my not-on-the-papers year, the
collaboration published a really big paper on correlations between the arrival
directions of the highest energy cosmic rays, and certain objects, active
galactic nuclei (giant black holes at the center of other galaxies). This
didn't just get published in Science, it made it to the cover. It was a step
towards working out where these mysterious particles are coming from, and what
mechanism might possibly exist to accelerate them to such energies! I was kind
of bummed not to have my name on that paper (though even at the time thought
the one year rule made sense).

The publication had been hotly debated internally. It had received all
conceivable internal scrutiny (the author list contained over four hundred
names). There had been a stark debate about what the right statistical
significance was for claiming a discovery. Astronomers were used to less
rigorous requirements than the particle physicists that together made up the
collaboration. Ultimately, it was decided to claim a correlation. (A nil
result would have been published as well.)

Almost from the day of publication, the statistical significance of the
result, but in data continuously collected since, started to diminish. I was
involved in running the nightly data reconstruction, and often times we would
huddle around one of the workstations on the morning, to check whether a new
high energy event increased or decreased the significance. It kept going the
"wrong" way.

The collaboration went into a frenzy to double check everything. They redid
the calculations, checked all hardware (a field of water tanks and electronics
3000km^2). Had lots of internal conferences with heated debates.

Ultimately a note was published describing the staggering decline of
significance. If I remember correctly, my name was on the author list by then
and I got to joke that I knew it all along.

I'm no longer in the field. Since then, the collaboration has published a di-
polar asymmetry of the arrival directions of the highest energy cosmic rays.
That proves something we already pretty much knew: these are not particles
from our galaxy. That was a meaningful discovery. But I'm not aware of a
publication with as exuberant a discovery as the source correlation.

~~~
smueller1234
NB: I'm not insinuating wrong doing. These were the most brilliant and
dedicated people I ever worked with, and with flawless integrity. The choices
around publications I describe were scientifically and ethically sound.

------
micheles
I left academia in 2003, after 3 postdocs in Theoretical Physics (Quantum
Field Theory, Thermal Field Theory, some Cosmology). The reason was certainly
not the money, rather job security, it is kind of stressful to change
continent every two years and not being sure of where you will be next year.
All that with the very probable outcome of being left without a position.
Also, I found I girl that later on I married.

So I started studying Python and since them I got various jobs as a developer,
including 7 years working in Finance, where I did a lot of Postgres and also
some web programming. In the last 6 years I have been doing numerical
simulations for Earthquakes, helping the geologist on the IT side of things
(like parallelization and performance). The funny thing is that all work in
Physics I did had nothing to do with computers, except for writing papers in
Latex. I was doing analytical calculations with paper and pencil, since
Mathematical was not good enough (found a lot of bugs in it when computing
integrals).

------
vanderZwan
I'm not a physicist, but a physics drop-out. However, I am still in touch with
many of the people I studied with. Only a few stayed in physics in the end.

One thing I noticed is that because physics has multiple decades more
experience with dealing with big data compared to just about every other
scientific field, a lot of physicists who jump ship tend to end up in a
position where they can apply that expertise.

I worked as a programmer for a molecular neurobiology research group for two
years. Biology is going through a kind of Cambrian explosion of new data
(especially when it comes to anything that involves genetics). So it's
probably not surprising that a number of people at work told me that it is
_extremely_ common to see physicists switch to biology because that's where
all the exciting new research is happening, with new theories and discoveries,
and lots of people who are very happy to steal whatever the physicists have
already figured out about how to process and interpret mountains of data.

~~~
sachin18590
BioPhysics was all the rage in my university few years ago. I haven't heard
much after, but genetics and big data have been at the forefront for a while
now. There have been a bunch of recent YC companies in this intersection as
well.

------
jqgatsby
I am not a physicist, but one thing I've noticed is that the physicists I know
tend to be very good at Mathematica, and if they happen to transition to a
data science role, then Mathematica is sort of this secret weapon that they
have.

I've been learning it for the first time recently, and there are data science
problems that are somehow tractable in Mathematica that were very hard for me
to do in Python. Some of this stuff, like FindDistribution, seems only to have
been added in the last few years. The random process library is really amazing
as well.

~~~
mensetmanusman
Mathematica is amazing for visualizing math. 6 lines of readible mathematica
code can compile into 100 lines of unreadable C.

Any blog posts on mathematica helping make data science problems more
tractable?

My thoughts are mathematica is good at intuition building, but not fast enough
to deploy without converting into subsequent languages.

------
batbomb
BS in Applied Physics. I work at a national lab (9 years now, since I was 25)
on astrophysics projects in a Physics division, but mostly I work on software
for data management, workflow management, data access, databases (multi-
petabyte distributed and otherwise), and sys admin stuff. Lots of python and
java, lots of kubernetes as of late. I occasionally drop down to working on
things related to data pipelines, mostly working on improving some internal
software interop with pandas. I don’t really touch science code, but the math
background still helps, and is sometimes required. Sometimes still have to
deal with ROOT.

The physics background means that, even if I don’t understand the exact
science of why my coworkers are working on, they don’t have to worry about
explaining everything to me.

Even my boss forgets I don't have a PhD. There's a huge need for people that
are really good at software development (relatively speaking) that don't get
tripped up by the physics, whatever it is.

~~~
jonahss
Where should people who are good at software development but don’t have
physics degrees look for jobs where they can use their expertise to help
science/academia rather than dating apps for dogs?

~~~
batbomb
Disclaimer: Most my professional background is in HEP/Astro, outside that find
I might be a bit off.

Basically labs/institutes and large universities that get government funding
looking for software developers.

National Labs (though it's getting rougher), flagship universities with large
labs or targeted programs like MIT (Lincoln Lab), Johns Hopkins (JHAPL,
STSCI), UIUC or UChicago (NCSA, UChicago pioneered much of grid computing but
some of that was Argonne if I remember too), USC, and especially smaller
labs/institutes or even specific projects at large universities. You can also
look outside the US, France (INRIA, IN2P3) and Germany (Max Planck institutes)
can be good places, Italy is okay, the Netherlands is good but not as large,
UK is good, Australia is okay. In Europe you can't expect to do anything with
CERN unless you are working at a National Lab in the US generally. Japan does
tons of stuff but most people I know who have worked on Japanese projects in
Japan don't enjoy it as much.

Secondary jobs are also a possibility near big national labs (Bay Area, New
Mexico, Illinois), but usually that's less software and more big-E
engineering.

Materials science and bio have a different layout but much of the cutting edge
of that tends to be located near light sources these sorts of facilities too,
and the need for software devs is growing there.

~~~
jonahss
Thanks for the reply!

------
frob
I got my PhD in mid-to-late 2014 in Heavy Ion Physics (colliders with
gold/lead/copper/uranium in addition to protons). We wrote a lot of code in
c++ for on-detector selection, data-harvesting, and ultimate analysis and
graphics rendering. Due to the dearth of jobs in the area and my spouse having
a great, well-paying job locally, I decided to join the startup world. I found
a little company that wanted a generalist (i.e. data, software, ui, wrench
turning) and became employee #10. Three weeks later we begin the acquisition
process with one of the FAANGs. Fast forward a month and a half and I'm now a
SWE there with no formal CS training. Additionally, I'm automatically a L4 due
to my PhD. This leads to pain. I'm doing the best I can to get work done, but
I have a physics mindset where right is 100x more important than fast. That's
the exact opposite of working at one of these companies. First half: meets
expectations with an informal note that I was on the edge but they understood
my position. Second half: meets most. One of the biggest punches to the gut in
my life. I start fearing being fired every day (I work in an at-will state but
I had yet to understand the costs associated with hiring and firing an
employee). I overwork myself to get to two halves in a row of meets all with
ALMOST exceeds (with a note of, "if only it weren't for that one week in the
half where you shut down due to stress"). I go through a couple of reshuffles
until I somehow end up on top of the stack due to some combination of
attrition and management issues and get that L5 promotion. I've been riding
that for a bit now and I'm coming out on the other side of this journey with a
much better understanding of the software development cycle, modern
technologies and frameworks, MVPs, product-focused thinking, rapid iteration,
the value of good tooling and IDEs, how fast can sometimes be more valuable
than perfect (with the right caveats), and a strong level of ML experience on
both the product and training sides.

------
asdfasdfdavid
Still recovering from the stress of a 3 year postdoc, working a minimum wage
job.

~~~
hpcjoe
That sucks. When I graduated (mid 90s) I had heard of some pre-tenure profs in
physics working as waiters over the summer to make money.

------
dhruvmittal
Money aside, there is a sort of more systematic reason we see physics MS/PhDs
bailing to industry.

Though theory groups in general tend to use computational simulations as a
tool to complete calculations, groups that develop novel computational methods
and techniques tend to be headed by younger, more junior professors. These
groups are typically well-funded and do very exciting (trendy? cutting edge?)
work with distributed computation, machine learning, neural networks, etc, so
they tend to pull quite a few students.

While these computational groups tend to bring in funding and are well-staffed
by excited grad students, the junior professors leading them tend to be
marginalized by the more traditional, seniority-focused establishment. Which
is to say, a new PhD might have a lot of trouble landing a prestigious postdoc
because a) their adviser might have been too young to have high name
recognition outside their field and b) departments might place limits the
amount of staff for these more junior professors/young groups doing exciting
computational work. This is, of course, on top of the overall scarcity of jobs
in academia.

But there's no such job scarcity in industry-- especially not for stats-smart
programmers with years of experience a) wrangling data in python, b) writing
fortran that runs on distributed clusters, or c) designing algorithms to solve
/approximate hilariously expensive problems. Advisers know this and point some
of their students who might thrive more in industry than academia towards that
route.

(Anecdote: And of course, as a physicist who builds models/simulations in
industry, I can speak a personally a little re: thriving. If you're someone in
love with solving disparate problems, you're unlikely to find that in
academia. Some of us learn in graduate school that we can't spend our whole
lives-- or in my case, more than a few months-- solving one problem. Academia
just... didn't seem like something that would be worth fighting for.)

I assume that this will gradually change as there's turnover within physics
departments and we get more computational-first professors with seniority (or
even in leadership). There are a few departments with better-known professors
you can see it happening now. Universities are spinning up incubators and
institutes for computational research. Physics departments are just slower to
adapt to new developments, and the hierarchy of theorists can have more to do
with seniority and internal politics than it does with technology.

~~~
O_H_E
From someone who _might_ be pursuing a physics degree soon.

> Some of us learn in graduate school that we can't spend our whole lives
> solving one problem. Would you please expand on this. I am not sure if you
> meant that problems are hard enough or what.

~~~
comicjk
The nature of a PhD is to study one sub-discipline long enough to reach the
edge of human knowledge, and then expand it. Often this is so difficult that a
set of multiple diverse projects is not practical. But not always; in my PhD I
worked on both astrobiology and solar cells. It helps to learn versatile
methods that apply to a range of problems.

------
ktpsns
Academic trends are ruled by money. In theoretical physics, computer
simulations is where the money is. For the students, that means they are doing
computer science related work, frequently the physics part is lacking. The
route to leaving physics and using the data science capabilities is then a
straightforward one.

~~~
killjoywashere
I work with a few PhD physicists, one is a technical program manager in
applied optics and ML; one is an individual contributor in optics and ML, and
the other one is pretty straight stick ML. I bailed on physics after my
bachelor's, ended up in medicine and now ... work in ML, with them. I don't
think I ever heard anyone tell me, or any of my classmates "you should get a
PhD in physics". It was something one did to do it. Because it's there. But
even 20 years ago, the guidance was to look for jobs in finance or computers.

~~~
rodolphoarruda
>20 years ago, the guidance was to look for jobs in finance or computers

It was 2003 when the company I was working for acquired InterBiz from Computer
Associates. By the time we were integrating teams from both companies I met
this guy who were the guru, the #1 product manager from one of the most
profitable lines of business that the company had. I remember the shock in
people's faces when he, a Portuguese guy, said he's got a PhD in Physics, in
Germany, in a second language to his own, but then ended up developing
Warehouse Management Systems (WMS).

------
Crossno
After grad school I got some very good advice from a mathematician: If you are
good at math, don't go where they are good at math.

He convinced me (and a few other phd physicists) to help him tackle the
problem of industrial controls for heavy industry (think large refrigerated
warehouses, steel refineries, food processing, etc.). We we're all graduating
so we decided to give the startup route a try. Since then we've been
designing/deploying cloud-based control software to regulate the energy of
these huge power consumers.
[https://www.crossnokaye.com/](https://www.crossnokaye.com/)

In the day-to-day its more data science/computer science than physics but the
core models we design are physics based so our white boards always have some
derivations on them.

~~~
dkmn
> After grad school I got some very good advice from a mathematician: If you
> are good at math, don't go where they are good at math.

^This. LOL. IMHO a lot of opportunities arise at the borders between fields.
This helped me put my finger on it; physics and its related math are a great
basis for interdisciplinary work. Selling that idea to SW-company HR and
hiring managers can be hard, in my experience, but startups more often benefit
from versatile employees early on. Cool business idea BTW!

------
s9w
I have a degree in physics and work as a software dev (c++). No one cares for
physics in the industry, but it's indirectly taken as a sign that people
aren't morons. I never used anything I learned at university at work. About
half of my colleagues have physics or Math degrees, other half EE or CS.

------
Cd00d
Realized during my first postdoc that I wasn't going to make faculty in my
desired geography, so took a second postdoc to buy time to figure out how to
pivot. Worked at a couple hardware startups that didn't work out (one failed
and one had R&D eliminated by acquiring company). After the second layoff,
with a 5 month old, I looked to transition to something in my city (NYC) that
was far more stable and safe.

Now I do data "science", and create data products.

The main downside is that now that I don't have a lab, but only a laptop, my
eyesight has changed dramatically :) Upside is pay and work/life balance.

~~~
plaidfuji
> eyesight has changed dramatically

Like how much are we talking here? Do you feel like you've found strategies to
mitigate that?

~~~
dkmn
Use positive-diopter ("reading" glasses). If your eyesight is normal, you
might need to get an optometrist to cut you a mild positive prescription, as
the lenses sold over-the-counter in US drugstores are generally stronger than
a young person would use in this context... But you can try one of the milder
ones and see how it fits your working distance.

Such lenses will "push out" the focal distance of your screen, so it is
optically closer to infinity. If you already wear negative-diopter glasses,
for nearsightedness, you can get a milder negative prescription for close work
or check out the new "computer" lenses that are multi-focal and give you
different zones for nearer, farther, and in-between.

Your eyes "work" to focus more closely-in, via contraction of the relevant
muscles to thicken the lens. So prolonged close-work, such as with a computer,
will cause fatigue and eye strain. There is also decent evidence, though it is
debated, that prolonged exposure results in a trophic response and acquisition
of myopia (nearsightedness). I've seen this empirically multiple times.

Wearing glasses for nearsightedness while you're doing close work is a double-
whammy. They make things look even closer. At the minimum, don't wear these
glasses if you don't need them at the computer.

Hope this helps!

BTW if you don't entirely trust ergonomic advice from a random dude on the
internet, your local optometrist can help. There is a type called (in the US)
a "behavioral optometrist"; these folks generally are more focused on
optimizing for occupational needs like this, vision therapy etc.

PS. Hey, just realized this is a physics audience! You guys can figure this
all out yourselves with the thin lens equation... :)

------
karasinski
I have a bachelors in Physics. When I finished my bachelors I took a long look
at my odds of getting a job in physics, then moved to Silicon Valley and
marketed myself as a software developer. I'm now about a year out from
finishing my PhD in Aerospace Engineering, and I've been working at NASA for
the past three years.

It's nice to read all the other survivor stories in this thread of those of us
that spent years studying to be physicists only to be crushed by reality.
Physics is tough, and I'm happy to read that many folks here have found
success in the field.

~~~
sbussard
This is one of my favorite posts. I really enjoyed working with the IRG at
Ames. A lot of people at Orbital Insight came from Ames.

------
sbussard
I'm a Software Engineer at Orbital Insight. Most of my work time is spent
porting principles from Category Theory and Automata Theory into front end
codebases. Problem solving techniques from Physics come in quite handy, and
are a differentiator from 90% of software engineers. It's all about optimizing
for the right variables at the right time. Often times delivery to market
supersedes code quality, but in the long run you can make software that's
better than anything else out there if you apply yourself and use the right
principles.

~~~
_mhr_
I would be interested in hearing what specific principles from physics are
useful in software development, as a non-physicist.

~~~
sbussard
Identify relevant information you have, identify what success looks like, and
apply first-principles to get there. Every step consists of working with only
the information required. It's a very empirical approach, but it cuts through
the cruft and gets to the point.

[https://medium.com/the-mission/elon-musks-3-step-first-
princ...](https://medium.com/the-mission/elon-musks-3-step-first-principles-
thinking-how-to-think-and-solve-difficult-problems-like-a-ba1e73a9f6c0)

~~~
_mhr_
Thank you!

------
dangirsh
Finished a bachelor's in engineering physics in 2014. Chose to pursue a career
in software/aerospace instead of continuing in physics.

I then left aerospace in 2017 for a quantum computing startup [1]. I'm
currently focused on simulation software, where my physics background is
certainly useful.

I like to think I'd still pursue a physics PhD if I became sufficiently
obsessed with a specific topic.

1:
[https://news.ycombinator.com/item?id=19281922](https://news.ycombinator.com/item?id=19281922)

~~~
tobmlt
Are there Quantum Applications & SDK positions open? I am planning to apply
within the week. (PhD engineering - optimization for my thesis, lots of
hydrodynamics at school and work. I started life in aerospace incidentally)

Also, I'm pretty happily obsessed with physics. It's really heartening to read
so many Physics folk ended up in software. I kick myself for not going for it
sometimes. Then I buy another book.

------
Balgair
Here's another story that ain't so glamorous:

BS Physics 2008. Somehow landed a 'dream' job with a major DoD contractor,
despite the recession (total miracle). They closed up our plant, due to the
recession, in 2010 and I moved to a smaller contractor. Got through the whole
clearance process only to find that my (now) spouse was a lot better choice
than the rest of my team. Good work, but the heart wants what it wants. Jumped
with no safety net, and got into neuroscience where my spouse got into grad
school. Worked there for free for a few years and got into grad school. Boy,
was that a mistake! Horrible grad experience in neuroscience and quit with an
MS. Was unemployed for about a year with a nasty depression and health issues
in the family. Finally working in DBA stuff and data science. Still love
bio/medtech and neurosci, but there just aren't the jobs here (need to stay
local due to family health issues).

Overall, not that bad considering the recession, doing about average against
my other graduates of 2008. Still, the corporate DBA stuff is ungodly boring
and the family health issues aren't a snack.

In the end, we all try really hard, but kids, health is everything. Everything
else falls to the floor in the face of health issues.

------
sachin18590
I have a bachelors and Masters (2013, with Non Linear Dynamics as MS thesis)
in Physics. Have always loved the field and the mathematical rigor involved.
However, chose a strategic consultant job as the pay was very lucrative. Got
disillusioned in a year and pursued a masters in CS/AI and worked after for an
year in ML. Started a company and got acquihired into an engineering
managerial role after.

Looking back perhaps one major regret I have is how far and fast I am moving
away from fundamental sciences. I don’t think I can leave the bay area or
coding anytime soon, but nonetheless, I have started to look out for ways to
stay involved with the world of physics in as many ways possible

(OP here) Quite amazed to see the number of physicists here and it is very
heartening to see so many of you doing so well in such a wide variety of
fields. Especially since during my college days physics was considered to be a
slightly dangerous choice from a job/career growth perspective

------
evanb
I have a PhD in nuclear astrophysics, did a three years postdoc at Lawrence
Livermore National Lab doing lattice QCD, and am 2/3rds of the way through a
three year postdoc at Forschungszentrum Jülich, between Aachen and Cologne,
Germany, continuing my lattice QCD stuff in pursuit of first-principles
precision nuclear physics and applying lattice methods to the Hubbard model (a
model of electrons hopping on spatial lattices) which likely has applications
to graphene and potentially high-Tc superconductivity and other crazy
materials.

I cannot find a 'real' (meaning permanent) job in my field. I applied to about
50 tenure-track university and college positions and staff scientist
positions. I applied for early-career fellowships from the Royal Society, CNRS
(France), the Helmholtz Gemeinschaft (Germany), among others. Almost everybody
I know thinks it's crazy that I don't have a job yet, but nobody has the money
to create one. So I'm moving to UMD for a 2 year non-tenure track Research
Assistant Professor job to give me 2 more cracks at the job market.

Lattice field theory is a computational technique by which we can extract
approximation-free, fully non-perturbative from quantum-mechanical theories
(I've described it on HN in a variety of comments, see eg.
[https://news.ycombinator.com/item?id=15782932](https://news.ycombinator.com/item?id=15782932)).
We use an enormous amount of leadership-class computational power.

Physics is now very computational (even theory), and often works with data
sets that make industrial 'big data' problems look like toys. I mean, one of
our lattice QCD calculations produced hundreds and hundreds of terabytes of
intermediate results. Data analysis, correlated analyses, and all sorts of
things that were old hat for physicists suddenly became lucrative. And
presumably it's a lot less soul-sucking than further "improving" high-speed
trading.

------
scotradamus
Dazzling managers by multiplying matrices together and calling it AI.

------
analog31
PhD experimental physics, early 1990s. Today, I work for a company that makes
scientific instruments.

A lot of successful physicists have stories about unorthodox career paths and
lucky breaks. This should be a red flag for anybody considering study of
physics. But maybe it suggests that exploiting opportunities and lucky breaks
is part of what physics education is about. You have to decide if you want to
live your life that way.

Why didn't I go into engineering? That was kind of an accident. Note that when
I was in high school thinking about what I wanted to do, the digital
revolution had barely begun, and maybe engineering still seemed a bit stodgy
to someone living in a sleepy suburb with little exposure to the world at
large. I had intended to major in math at a small college with no engineering
school, and ended up adding a physics major and heading to grad school. I
loved experimental science, and thrived in the lab. My parents are both
scientists, and had pretty good careers, so there was that whole role model
thing.

At my present job, we have a full engineering staff, including programmers.
Why do we need scientists? There are actually a lot of scientists working in
"engineering" organizations. I've noticed that the scientists tend to be more
multidisciplinary and quantitative. Whatever the difference, I think it helps
to have both perspectives. I get handed weird, unsolvable problems, that can't
be categorized. I develop a "system" view of how things work. I work on
manufacturing problems, customer applications, and so forth. I'm one of the
"math people," and I handle weird things like understanding measurement noise.
I actually _like_ theory.

When I think about whether I should have been an engineer, I remind myself
that I might have failed at it.

I've been pretty lucky. My job isn't glamorous, but I've had a good career,
and my job has never been super intense in terms of stress or hours. I enjoy
my evenings and weekends.

------
frickinLasers
It made me pretty sad to learn the physicist who authored the Britney Spears
guide to Semiconductor Physics is now in SEO. He is apparently in it for the
money.

[http://britneyspears.ac/physics/about/about.htm](http://britneyspears.ac/physics/about/about.htm)

~~~
mjfl
This is a really bad attitude. People should be able to take care of
themselves and their families financially. You shouldn’t shame them for not
living like monks.

~~~
andrepd
No, _this_ is a really bad attitude. I cannot stop them from selling out or
doing whatever they will, but it is probably a good thing for people to
"chastise" or "shame" people for selling out to activities that bring
absolutely no net positive value to the world (and in many cases very much a
net negative). Same goes for working in fintech and stuff like that.

Plus, the parent never even "shamed" anyone, they just said it makes them sad
that a person they admired is doing SEO of all things.

~~~
mjfl
What do these people owe the world? Nothing. How much net positive does the
average person due to for the world? Not much. Are intelligent people your
slaves? You mean some smart person who's done some good for the world should
be ashamed for trying to afford good schools for their kids? That's the _worst
possible_ attitude.

~~~
andrepd
I didn't say any of those things, and I'm not interested in overly
confrontational discussions.

~~~
mjfl
then precisely what would you say, to the scientist with children to feed?

~~~
frickinLasers
There are plenty of ways to make a living and still be a net positive for
society. I wasn't shaming him, but I'd love to raise my children in a culture
where the focus is helping not just yourself and your possibly undeserving
offspring (e.g, the university bribery scandal or countless other instances of
corruption), but the rest of humanity as well. I think we'd all be a lot
better off.

But at least this guy is honest about his goals.

------
jordansmithnz
I have a bachelors degree in physics, but ended up developing iOS apps.

Physics turned out a little more dull than I expected (I wanted something with
more creativity), and an app I’d started working on as a hobby turned into a
full time income, so I started pursuing this instead
([https://classtimetable.app](https://classtimetable.app)).

I took a few full time iOS jobs, continued to improve my engineering skills,
and I’m currently working for a top five tech company on a popular iOS app.

Physics certainly taught me a few skills that I use on a daily basis - math
and problem solving are good examples. I didn’t move to software specifically
for the money, but comparatively physics seemed a little more dull, and
software seemed to have new and exciting opportunities (like the app that I
started building as a hobby).

------
boothby
I'm a physicist-adjacent mathematician, a working topological graph theorist
at DWave. I'm an architect (more like a quantum architect, elbereth) for the
hardware team and also a developer / researcher in algorithms.

On the hardware side, I enumerate and evaluate qubit topologies, and solve
combinatorial puzzles of packing of qubits, couplers, couplers and their
control structures, for my team to implement said topologies. Our processors
are a fun mix digital and analog, and in development, that's "digital until
things get too analog"

On the software side, I research, write and maintain embedding algorithms
which are used to fit problems onto the chip, and I also work in hybrid
quantum / classical optimization and sampling algorithms.

~~~
Mithriil
This kind of work seems very pleasant on the mathematical side.

May I ask in what company do you work? Also, what kind of study did you go
through? I'm almost at the end of my bachelor in mathematics, and I want to
get close to physics and quantum computing. Your experience seems relevant!

------
sballin
I'm a PhD student studying turbulence at the outer edge of plasmas in fusion
experiments. Turbulence degrades plasma confinement, which makes it difficult
to keep the plasma burning and produce power. I'm interested in whether
different machine designs or operation procedures can help reduce turbulence.

At the moment I'm analyzing data from W7-X in Germany. It's a really cool
device off the beaten path of tokamaks but seriously catching up in
performance [1].

[1] See fig. 15 of
[https://iopscience.iop.org/article/10.1088/1361-6587/aaec25](https://iopscience.iop.org/article/10.1088/1361-6587/aaec25)

~~~
pontifier
Any chance you'd like to chat with me about a new type of fusion device I'm
working on? At low density the theory is more like a collider, but as density
increases, it might become turbulent and disrupt the periodic motion I expect.
I'd love to hear your thoughts on it.

------
stargazer-3
Finished an astrophysics PhD (observational studies of massive star formation
in the Galaxy) and switched to a Data Engineering job half a year ago. Post-
PhDs becoming data scientists is still a big thing, as career options are very
limited.

The field was really interesting, but building a carrier in it is a pure
lottery - hard work and talent alone won't cut it, you need connections,
politics, and salesmanship skills to get a permanent job.

On top of that, there were probably only three job openings a year (in the
whole world!) that I was a good fit for. Money factor did not come into play
at all - junior dev salary is often lower than the postdoc one.

------
ummonk
Bachelors in physics. FANG software engineer now. Gotta afford Bay Area
housing.

I'll probably retire early one day and then work on other interesting stuff I
like as well, e.g. rocket science which I did briefly.

------
wuliwong
I received my Ph.D. in physics from Georgia Tech in 2010 doing computational
studies of synchronization of coupled neurons. After a year in industry, I
went off to build my own startup. It was in the social, music sharing space. I
was not able to raise money, joined a startup and have been doing software
engineering for the web ever since. Recently, I've been learning ML in my
spare time, it seems to be a nice intersection of some of the math I learned
in physics and my more recent software engineering work.

>are there any other reasons for this as well?

I love physics, but I also love a lot of other things. I was a musician well
before I was a physicist. My exit from "doing physics" was driven by a desire
to start my own company. I found I really enjoyed developing software for the
web, it was a nice blend of technical and aesthetic.

I think the draw of physicists towards data science is because of the familiar
mathematics. My experience in recruiting data scientists is that candidates
that have formal degrees in data science generally have little experience
outside of school. My assumption is that these degree programs are relatively
new.

>Of late, except for few headline-friendly fields (colliders, quantum
computing, gravitational waves and astrophysics in general), I don't get to
see/relate with a lot of activities in Physics

Do you think this used to be different?

~~~
dkmn
> My experience in recruiting data scientists is that candidates that have
> formal degrees in data science generally have little experience outside of
> school. My assumption is that these degree programs are relatively new.

A friend (also a physicist, though harder-core) who is a now a data scientist
opined to me that the current wave of cross-trained data scientists will be
replaced by "kids with their new data science degrees from new data science
programs". He didn't mean this ill, but simply thought there was a window of
time to make a shift.

Your comment implies you are actually seeing relevant benefit from people with
more diverse experience. Can you elaborate? Do you mean lots of hands-on data
science per se, or simply a broader practical experience?

Edited: for formatting

~~~
wuliwong
Sorry for the delayed response, hopefully you check back. :)

My experience with hiring is limited, I've only hired 3 data scientists. One
had a bachelors in physics and masters in data science. One had a masters in
data science (i forget her bachelors degree) and one was a PhD nuclear
engineer.

I think the main difference was "broader practical experience" as you said.
That' was mainly my point. Although my sample size is small, I was just making
the point that the degree programs are so new that graduates can't have much
real work experience. Obviously if someone returns to school after years in
industry and gets a MS in data science then they could have work experience as
well.

------
Insanity
I'm not a physicist but studied physics before jumping to programming.

A surprising amount of people whom I studied with ended up as programmers
themselves, after finishing the physics degree.

~~~
majewsky
My first degree was in physics as well, and I also ended up in IT. At SAP, to
be exact. Urban legend among German SAP employees has it that SAP expands to
"Sammelplatz arbeitsloser Physiker" (gathering place for unemployed
physicists).

~~~
shifto
I know a few people at SAP (NL/DE) who are overqualified for the job they are
doing and seem bored/uninterested. What's an insiders perspective of this?

~~~
majewsky
I guess it depends on where you are in the company. I have been under two
different managers since I joined 7 years ago, and they both keep their
employees on a relatively loose leash. As long as the required work gets done
in an orderly fashion, we get to choose our own pacing and attack angles.
Makes for a pleasant work experience most of the time.

Then again, I guess I value stability and predictability in my day job a bit
more than most Silicon Valley startup employees.

------
surgi
I have a Masters in Physics and Applied Mathematics. I started to work (edit:
original role was Frontend Engineer / Architect) for US-based startup 6 years
ago, 4 years ago founded development office in EU (for the same startup),
became engineering manager and eventually GM of that office (75 people back
then). Last year we exited for reasonably big number, now I help to finish the
integration into the corporate and partially cover SW Architect role.

------
godelski
I have a B.S. in physics. I worked as an engineer for awhile and then landed
in a PhD program doing computer science. There's better job opportunities
here, (substantially) better pay, and I'm still able to work close to the
science (doing HPC stuff).

I also have always liked computers, so the switch is a good fit for a lot of
reasons. But I'll also say that it feels like a completely different world
(same with when I worked as an engineer).

------
TheHideout
I did my undergraduate in Aerospace Engineering and Astrophysics, then
graduate work in Space Systems Engineering and regular Systems Engineering. I
also did plasma physics research at Princeton.

Now I'm doing aerospace vehicle modeling and simulation in MATLAB and C++ and
primarily work with other physicists. This is by far the most enjoyable work
I've done and the pay is excellent - my salary is 3-4x the average household
income for the city I live in.

------
spamcamel
Here's my story. I earned a BS in Physics and Mathematics in 2012. My grades
were good and my academic advisers suggested I apply to PhD programs, however
I knew that a PhD wasn't for me. Instead I went into an MS in applied physics
and got an internship at a laser and semicon equipment company that paid me
more than the cost of my MS. The company was going through a cycle of poor
business results while I was an intern there, so after graduation in 2013
things didn't work out and I left to find other work. I ended up as an
applications engineer at a synthetic diamond company. When I started at the
synthetic diamond company I had some really interesting projects to work on.
In the 5 years since, the dynamics of the business have ended up with me in a
product management / business development role, which is OK but I'm not
especially passionate about it. Also, the compensation, skill development, and
future prospects of my role leave a lot to be desired. Because of this I'm
looking to make my next career move soon.

I've spent a lot of time over the past year thinking about what my next move
should be. In this time I've also become hooked on coding, mostly python. My
current plan is to pivot into (you guessed it) data science. I expect that
this will lead to better pay, vastly more potential employers, and allow me to
get back to working on interesting projects.

In hindsight, if I could do everything over I'm not sure I would go into
physics. What I've seen is that physics is mostly a field of niches that are
filled by specialists. To go far in physics you need to become a specialist,
however this can really limit your options later. I think this is why many
physicists eventually find themselves going into other fields like data-
science.

------
davrosthedalek
(PhD 2010 in Nuclear Physics) I stayed in Academia, right now I'm trying to
survive the first year as an Assistant Professor. Teaching is a lot more fun
than I thought it would be. My research is two fold: One half of my research
is focused on precision measurements of the proton form factors (proton radius
puzzle, form factor ratio puzzle), the other half on streaming readout for
next-gen experiments.

~~~
pontifier
Interesting research... I hadn't given much consideration to the interior
structure of protons and neutrons before.

~~~
davrosthedalek
It is! If you look around you, essentially all the mass you see is generated
dynamically by QCD inside the nucleons. And while we believe we can write down
all the rules for QCD, we can not solve the equations well enough that we can
reliably predict outcomes (emergent properties, like the masses, size etc),
except if we do lattice QCD, which is essentially a brute-force numerical
solution.

------
andrepd
I switched fields to mathematics / theoretical computer science (theorem
proving, to be exact). The field I had worked on in physics didn't excite me
to pursue a phd/carrer, and there were no opportunities (that I could get
into) in fields that did interest me. Plus, I always had a string interest in
maths, so... It has been a good choice so far, but I do sometimes miss working
in physics.

------
rubidium
Physics PhD, 2015. I knew from the start of my PhD that I didn't want to
pursue academia. The PhD was like being in a band for 6 years and doing really
cool stuff.

Went right into industry after PhD working for a life sciences company in
systems engineering (the product design type, not the computer networking
type). Now managing a small team designing robotic systems to automate
chemistry/biology research.

~~~
Balgair
I'm in a similar path, but struggling with it. Any advice on who/where to
apply to? I'm really into med/biotech and just love it.

~~~
rubidium
Where you at? Interested in doing actual biotech research (e.g. new biologic
drug development) or technology that enables the biotech research?

Best job-hunting advice I've found is "Guerrilla Marketing For Job Hunters"

------
semiquantumguy
BSc in Physics, MSc in Math dropout PHD in high energy physics (needed the
money) to work as data scientist on a major tv broadcaster in my country

~~~
eightorbit
I hear you, man. I hope that job is interesting, I would like to make that
transition myself. Cheers.

~~~
semiquantumguy
Well, the job is interesting, I spend lot of dollars on GCP =D. But if I would
have to choose, without money been an issue, I would go back to the academy.

------
lowsenberg
I have a PhD in physics and worked as a post-doc for a few years, until I left
for industry a couple of weeks ago. The last project I was busy with is
developing a massively parallelized image simulation code for scanning
transmission electron microscopy. It is open sourced here: www.stemsalabim.de

My new job in industry is consulting about HPC systems in the context of
computer aided engineering.

------
freeboson
Particle phenomenology (hep-ph) PhD in 2014. Left after 1st postdoc (3 years).

I had two kids during my postdoc and quickly became disenchanted with the
prospect of hunting for postdocs in a random part of the world. I was as
interested in statistics and machine learning techniques, so moving into
industry was not terrible. I still love the formalism of supergravity, but it
looks to be becoming less and less relevant in hep-ph.

You're absolutely right that data science is a common destination for exiles.
It makes the most sense because we get to still read interesting mathy papers,
develop computational tools. The mechanics are very similar for physicists (in
certain fields). Literally every single former physicist friend I have on
LinkedIn is working as a Data Scientist, except one who is teaching physics at
a private high school in NYC.

When I left my postdoc, I worked at a data science startup for a year, and now
I do general software and applied ML at google.

------
sandwall
Visualization of Ionizing Radiation. Ionizing radiation has been outside our
visual domain for far too long, with scintillators and computer vision
(semiconductor detectors + processing) we can shift the spectrum. --- This may
be more engineering than physics; I'm a clinical medical physicist and
sometimes the lines are blurry. :)

------
ssivark
Physicist (PhD 2017, hep theory/phenomenology) currently working as a
researcher (in the context of AI and robotics) in a Silicon Valley startup.

I am greatly excited about ideas at the interface of probabilistic inference
and quantum/statistical physics. On the one hand, these should help create
better ML models/algorithms; on the other hand, I believe that tools from
probabilistic inference will help better understand emergent phenomena in
complex systems. The former is what I'm focusing on right now, the latter, I
think might take a couple of decades.

When graduating with a PhD and thinking about what I'd like to do next, I
didn't think I was a good fit for life on the academic track (post-doc,
tenure-track, etc), given the kind of questions I wanted to think about and
the manner in which I wanted to pursue them. I also wanted to gain some
experience writing software and applying ML to real world problems (as a
"regularizing" effect on my theorizing), so I took the path I did.

I've come to realize that I'm a researcher at heart, and it's difficult for me
to _not_ spend time exploring new ideas. I just need to find the time and
space to do that, and I'm trying to structure my life so that I can.

> _Also I have noticed a growing trend of physicists becoming data scientists
> post phD. Although I understand the money factor, are there any other
> reasons for this as well?_

I think this has always been the case, at least as far back as software in the
'90s and then finance and now "data science" added to the mix. The typical
physics education/training makes one a generalist with a broad background in
problem solving and mathematical tools, and a flexible mindset, so that one
can adapt to be effective on the problem du jour, while there is a dearth of
specialists with the specific skills necessary. I imagine this overarching
trend continue to be true going forward as well.

------
Libbum
Finished my PhD in quantum computing three years ago now.

My first post-doc was in laser-matter interactions. We had an experimental
team that were focusing PWs of power on to ultra-thin films and separating the
substance into its constituent parts (electrons and protons I mean). The plan
was to increase the proton acceleration yields & make a consistent, tight
bunch (in the energy spectra) so that we could use it for next generation
cancer treatments. That's a long way off and no-one really has a good idea
what else we can do with this system.

That annoyed me. There's not enough application or relevance there. So I've
moved to Earth System science. I do a lot of global climate-economy coupled
models & attempt to implement climate models with human decision making as a
part of the system rather than some form of external forcing.

~~~
mensetmanusman
What wavelength was the laser setup?

------
cycomanic
There's really not enough long term jobs in academia so people have to look
elsewhere. Fortunately physics really sets you up for a lot of very different
jobs. I myself am one of the lucky ones, am working on a permanent position
(similar to associate Prof) at a Uni in Sweden. I was thinking of jumping of
academia and doing something else several times but somehow also a good
opportunity just at the right moment. One thing that is often easy to forget
is that there is so much more than the current hot topics that make the big
media splashes (quantum computing, gravitational waves, graphene atm). I work
in optics/photonics primarily in optical telecom. My work is much more
engineering-like than many of the above topics. Anyway the further along in
your career you come the more managerial work you do, same in academia as
everywhere else I think.

------
sonofaragorn
PhD in Experimental Neutrino Physics (2018), now a Data Scientist at a growing
start-up.

The transition was an easy one for me as I found a particle physics
collaboration shares many characteristics with a start-up.

One thing to note is that responses here will be biased towards physics people
that are more interested in the tech side than the physics side.

------
danbrooks
PhD in Applied Physics, 2018

Working on data science/ML now.

Started undergrad in 2008 and had to decide between CS and engineering
physics. Went with the latter because the school had a particularly strong
program.

Applied for PhD programs and national lab positions in 2012. The PhD route
seemed more interesting, so I went with that. Ended up spending 6 years on
materials simulation.

My peer group ended up going to academia, software engineering, and data
science. Technology companies seemed exciting so I interviewed for software
engineering and data science roles. Ended up with a company that predicts
accident risk from driving data.

I think that physicists are particularly well suited to the machine learning
space. I was trained to work systematically work through problems with no
analytic solution through clever approximations. This tenacity can help with
tackling problems in the machine learning space.

------
kiliantics
I finished a PhD in astrophysics this year and just started a job as machine
learning scientist, working in computer vision for a startup. I massively
under-anticipated how much this change would improve my work conditions and my
sanity -- and I haven't even gotten my first paycheque yet.

------
tdhttt
So I work in experimental particle physics and our daily tasks involve using
particle collision simulation tools(mg5+pythia+delphes), distribute them on
clusters, construct jet images using ROOT and finally experiment with neural
networks on those jet images. So as people pointed out, what we do had a lot
in common with the industry (where we learn from as well), which explains why
many people involved in this kind of research could land a data scientist job.
An interesting thing I've noticed is that many phD students in our group (with
little CS background) waste lots of time on getting the software running
(either locally or on cluster). You guessed it, we are trying to exploit the
power of docker to make it easier for researchers to run those simulation
tools.

~~~
pontifier
Any tips for a hobbyist who needs to do some simulations of a novel device?
Much of what I've looked at requires writing a lot of the simulation code from
scratch.

------
otras
I have a bachelors in physics, which I finished without doing any real
research or programming (not recommended). After a brief attempt to find work
related to politics/science in DC, I ended up working full time as a tutor
(mostly math and physics). I ended up learning front end engineering, which
was an uphill battle without programming experience, and after a few jobs, I
now work as a software engineer at one of the large tech companies.

My background in physics and math really helped in CS classes I've taken since
graduating, and the general technical background and comfort with math has
been very helpful in general. I do wish I had done more research and
programming in college, as I ignored them in favor of experience that would
line up better with work in DC.

------
d3ld0t
(PhD, Condensed Matter 2015) I am a quant at a trading firm. I have more daily
challenges and unsolvable problems than I have ever faced in physics.
Obviously it pays well but 12+ hour days take a toll. Beats working a post-doc
for N years, on the other hand...

~~~
anonu
Trading is a great place for physics PhDs because there is way more upside and
the rigor gained during your studies adapts well to studying the markets.

Also depending on what level of magnification you use to observe the markets:
they are just random. Maybe there is some consistent process that works today
on some subset of the market given a certain forward-looking horizon.

Then, that opportunity disappears at some other time in the future. Its
ephemeral and fleeting... and this is probably where the daily challenge comes
in.

------
ddavis
(5th year PhD student)

Working on my thesis analysis (collider based HEP), squeezing in time for OSS
development and prepping for a jump to software dev.

I love physics, but academia is in a rough place right now. Almost everyone I
know pursuing the academic career path has nightmare stories.

~~~
hpcjoe
[ghost of tenure track searches past]

The physics job market has been, and will remain in a "rough place". As with
all things, it is who you know.

If you want to stay in the field, network like mad. Get people to know your
name. Make sure you have work known to them. Get a great Postdoc with someone
who will make calls for you when you are done with your project, to help your
search.

Otherwise, computing is nice :D

------
sparcraft
I graduated with a bachelor's in Physics 10 years ago, but I have been working
as an actuary for the past 8 years.

Navigating the politics and culture of the academic world never really made
sense to me, and at the time I felt that I was not smart enough to ever really
make a meaningful contribution to Physics.

I do not regret studying physics; I think the mental stimulation and growth I
gained from those challenging 4 years of study have served me very well and
actually made me a smarter human. However, I am happy to apply the logical
rigor and analytical skills I learned to more simple and immediate problems in
business.

------
aznpwnzor
BS in physics 2014 (minor in CS)

my research in undergrad and applications for grad school were for
computational neuroscience (sort of the whole what would Feynman do if he was
still alive route)

1\. Didn't get into as many programs as I wanted.

2\. East Coast schools even explicitly told me that wetlabs were more
important (I semi-agree).

3\. West Coast schools were all being gutted for ML/Data science work.

4\. all the post docs and grad students I had worked with had switched to
doing ML

I deferred for a year and worked at a biotech startup doing neural network
simulations to prove the product worked and scrappy hardware startup things.

I've since been at a startup doing NLP for the last two years.

Don't regret the degree which to me is like a STEM liberal arts degree.

------
Anon84
I posted this
[https://news.ycombinator.com/item?id=19500510](https://news.ycombinator.com/item?id=19500510)
(by some former colleagues) just a few min before seeing your question

------
edouard-harris
Got a PhD from the University of Toronto, went straight into founding a
company. Went through YC last year (W18).

If I could do it over again, I'd have dropped out of grad school in Year 1.

As an aside - there are way more physicists on HN than I expected.

~~~
sachin18590
> If I could do it over again, I'd have dropped out of grad school in Year 1.

Haha. I can see how you are trying to take on the current education system
itself. To some extent, the education system has failed us to some extent.

------
physicsyogi
(PhD, 2005 in condensed matter)

I left academia after my postdoc and went to work at one of the think tanks in
the DC area for a few years. When I got tired of being a government contractor
and left to work in natural language processing and machine learning.

Now, I build machine learning software to detect financial misconduct like
insider trading. I also built software that helps law enforcement find minors
who are being trafficked.

Some of the mathematical and computational methods used in physics are used in
machine learning, so it was a relatively painless transition.

I'm also developing some mobile apps in areas where there isn't already
saturation.

~~~
elliekelly
I've been looking for someone with your exact skillset - applying NLP to
financial regulations. I'm an investment attorney turned developer working on
software that uses NLP to automate compliance for SEC-registered investment
advisers.

------
raziel2701
I'm about to finish my PhD (condensed matter) and I've reached the conclusion
that science is just a phase for many of us. My university hosts a Beyond
Academia event where companies come to recruit future phds, there's a student-
run club that brings in people outside academia that hold a phd to talk to us
about their path and current jobs, and as I've attended these events over the
last four years I see example after example of brilliant people simply having
to move on from science.

It's staggering to see your Stanford and your Harvard grad students become
scientific experts only to then work on improving ad delivery and how to move
someone else's money across the world to make the rich richer. It feels
wasteful. But there's no room for everybody, there's very little room for a
few and it's stressful, competitive and the path is riddled with anxiety and
mental illness, which I've had enough of already in grad school.

People move to data science because it's a transition into the software
industry that is eating the world. It's a safer bet, pays well, and has the
name "science" in it :) It helps people transition.

I've stalked many on linkedin over the years and it does seem that science is
just a phase, very few are still doing science. I think all these people liked
or loved research, but the combination of the nasty, petty environment
surrounding research, and the lack of permanent positions makes it very
difficult for someone to have a fruitful, worthwhile career in science in my
opinion.

I think we have a problem. We have a lot of smart people but we don't know
what to do with them. And I think no one up high is really questioning what is
the purpose of a college education and of a post graduate education in order
to modify these programs to better address a student's path. Academia changes
one funeral at a time, and that might be too slow to tackle some of the
problems we are facing.

I've really enjoyed the research aspect, but I have come to hate my
interactions with my PI who is sometimes too busy doing administrator stuff
and in my opinion sometimes refuses to see reality for what it is: Sometimes
to do modern research you have to invest in modern equipment. Sometimes I
don't understand why my data looks a certain way, why is anger the default
reaction to this?

I haven't had enough time and space to gauge whether or not this phd is worth
it, all I know is that I'm really excited to leave an environment that has
broken me a few times, and that continues to inflict pain on others I love.

------
physicsthrowaws
I am about to defend my thesis in physics and will probably start a data
science job afterwards (have had a few second round interviews, waiting for
offers/rejections). Note that my thesis work did not contain big data or
machine learning, but I have completed a couple of outside projects. I am in
the US and in a top tier university/program.

My perspective is that there is just not many academic positions available. I
realized I could do a couple of post docs for a few years and hope that
something opens up, or not delay the most probable outcome and start an
industry career sooner rather than later.

------
piadista
I have a masters in HEP and have experience working on the CMS experiment at
CERN for a year as part of my Masters.

I left physics for data privacy in 2017 (PhD programme).

The main reason was that I didn't really feel like I was tackling real issues
(real and immediate to society) if I continued work in physics.

The swap has been difficult and I am at a disadvantage wrt to colleagues that
come from more relevant backgrounds (e.g. Computer science or Applied maths)
but at the end of the day I feel a lot better about the contribution of the
work (to society) and also about job security in the future.

------
throwawy201903
I work on developing the ground system software that process scientific
satellite data for a space physics mission at NASA. They give us a lot of
freedom to balance emerging techniques from data-intensive computing backends
(things like containers, queuing systems, worker pools) with some fairly
sophistical numerical algorithms and statistical learning techniques. It is a
challenging and rewarding blend of hardware engineering, software engineering,
mathematics, and science. The experience of some of the people I work with is
amazing.

------
MLWithPhil
I got my PhD in physics back in 2012, after doing my dissertation in spin
dependent transport phenomena in magnetic materials.

got a job at Intel as a back end process engineer, which was pretty cool, but
I was then laid off in 2015.

These days I'm all about machine learning. Teaching myself by creating
content, and hopefully educating others. It's been really cool seeing the
overlap in some concepts (systems seeking minimum energy vs. gradient descent)
between ML and physics, and I'm hoping my background will pay dividends as I
get deeper into the field.

------
organicdude
BS Physics - I help bring non-drug health building approaches to the masses.

I get my information from scientific articles and approaches from clinicians
in the field even if you can't prove everything 1-to-1.

This approach gives me loads of credibility in a space that's filled with very
unscientific approaches.

I'm deepest in probiotics and digestive health. But the basics for improving
health are quite simple - good sleep discipline, clean water, understanding
signs of nutrient deficiencies, blood sugar management (even for non-
diabetics), stress management, movement.

------
probwalk
B.S in physics, total spent 5 years in biology (first MS then Ph.D program) as
I felt like there are lots of areas to be explored / studied there. I later
switched to statistics (and got a PhD in statistics) as the future of senior
lab-mate did not look bright to me (many years in Ph.D or post-doc stage, work
is more or less driven by publication pressure instead of science curiosity).
Choose statistics as I got good background in math thanks to the education in
physics.

Now my job title is "data scientist".

------
perlgeek
When I still pursued my PhD in physics, I worked on all-optical amplifiers
with phase noise reduction for fiber communication.

Now I develop software for a medium-sized ISP and IT outsourcing company.

------
qubyte
PhD in Physics (2009, quantum optics and quantum info, both theory and
experiment). After my first postdoc in Tokyo (mostly computational in C++
toward the end) I decided to move into industry and worked as a Node.js server
dev for a games company there. Got married!

These days I'm back in the UK (Brighton) and enjoying the arrival of my first
child. I'm still working in Node for API and unusual proxy servers, with some
Rust on the side. Enthusiastic about the indieweb movement.

------
jberger1613
PhD in 2012, Theoretical high energy particle physics.

Now I work optimizing fantasy sports teams and building websites that display
betting lines. Don't even like sports and I have no idea what anyone in the
office is talking about, but they are paying a lot for me to put buttons on
their website I guess. Leaves me a bunch of room for my hobbies. Offered to do
some actual math, maybe personalization algos or some AI stuff. Really just
need these buttons on the site is all.

------
lokimedes
I work on “AI” and Data Science at a European Defense Company. After 8 years
of Particle physics at the LHC it feels really old trying to do data “science”
and “big data” for the highest bidder. Oh how I wish for a Manhattan scale
project to save us from climate change with nuclear or something like that. On
the other hand I’m glad to have left the zero-sum game of post-docs for a
regular career track.

------
pacman128
PhD in physics, then visiting prof for EE/CS dept at major univ for a year,
then CS prof for 10 years at 2nd tier univ, then software developer for last
19 years.

I don't regret getting the PhD. Always was very interested in physics and
learned a lot. Did a lot of computational work which helped with the
transition to software. I still use the problem solving and some of the math I
learned for physics.

------
coldcode
Not in Physics, but I bailed on a PhD in Chemistry back in the early 80's and
became a programmer. I still do it today, but wonder what might have been if I
could have finished it back then. We have a programmer here with a PhD in Math
but that's even less well paying if you want to do theoretical stuff. Sadly
doing original research in any field is not a great career option.

------
iSpiderman
I have a master's degree in theoretical physics (2006), first went into
consulting. After two years I quit and implemented currency trading algorithms
for a small company for five years. After that, I tried something on my own
(not related to physics or coding) and slowly slithered into teaching.

Today I teach math and physics in high-school and feel like never having had a
better job in my life.

------
muvek
I recently finished my Master's in Physics (with a minor in Applied physics,
thesis in Astroparticle physics). I also have a BS in Physics. I've been
looking for a job for a few months now and haven't found anything interesting.

Where do you guys search for jobs, in Europe? I was mainly looking for a Data-
Science position, though I don't know ML yet (other than regressions ofc)

------
dsqrt
I got my PhD in 2013 (computational astrophysics / general relativity), I did
almost 6 years as postdoc at two different institutions, and I am about to
start a tenure track job. I write code for and run fairly large-scale HPC
simulations of gravitational wave sources for LIGO, such as colliding neutron
stars and black holes, and core-collapse supernovae.

------
soapboxrocket
I have a BS in physics but have never been in the "physics" field. I was tired
of school and left to make some money. I ended up in aerospace designing test
equipment installs for flight test aircraft to support certification. I have
spent most of my career in oxygen systems for aircraft and am now in the
process of starting a new aerospace company.

------
benjohnson1707
I wanna take the chance asking this question while having so many physicists
present in this sub: is the 'first principle thinking' approach to problem-
solving somewhat helpful outside academia? Is it a thing in the first place?
Seems super interesting as a non-physicist, however it's hard to judge from
the outside. Thanks for some insights.

~~~
mensetmanusman
It is for me. Product development and technology development with a deep
understanding of thermodynamics and energy efficiency can be valuable. Heat
kills a lot of things :)

------
fearhugs
(HEP Phd, finishing)

I'll also be joining the data science world.

There's some debate about whether it's the best or worse time ever to be in
particle physics. Either way I see a field that is overstaffed. Add to that
the fact that CERN accelerators are shut down for two years and we're in the
middle of the european strategy review.

Seems like a good moment to take some time out

~~~
whatshisface
My prediction is that the stagnation of physics will continue until the money
and prestige has dropped to 1800s levels. Then, the only people who do it will
be "true" in that they are motivated purely by curiosity. At that point the
next set of breakthroughs will come around because all of the emotional and
funding-treadmill induced blindspots that we have presently will be gone. Of
course this is assuming that somehow the experiments can be kept up even
through the funding decline.

~~~
MockObject
Meaning, science as a pastime for the idle wealthy, with no pressure to
publish or perish?

------
Anon84
(PhD, 2008 in Complex Systems)

While my PhD did start out as supposedly being on Spin Glasses it quickly
diverged to Complex Networks and what people would now call Data Science.
Since then, I've worked on Social Science, Epidemiology, Human Behavior,
etc... For the last year or so, I've been doing Data Science and Finance in
one of the Big Banks.

------
gjmulhol
Building physics and chemistry domain specific machine learning systems at
Citrine Informatics. It's the best of both the physical science world and the
data science world, has really hard problems, works with big companies and has
big contracts, and is a team where more than half the people have technical
graduate degrees.

------
perceptron2go
I have a degree in Applied Physics. Have been doing machine learning for a
while now. Currently working with a group that tries to decipher SETI data
using AI in order to popularize space related research.

You can look at the results here:
[https://intelactica.com](https://intelactica.com)

------
johndunne
I graduated from Physics at Manchester Uni, UK, in 2001. Damn good few years.
I never did anything that would qualify as a physics career and went straight
into coding, working at Sonicwall most of my career. I still keep on top of
the bleeding edge in physics, physics is still close to my heart :D

------
nicodds
I'm a physicist, with a PhD in physics. I worked for a while as a post-doc in
Italy and recently I quit University, working now in the private sector as a
Data Scientist.

I'm happy with the change, my life is more relaxed now. I've time for my
family, for me. I've also better and stable earnings.

------
softwarerero
As a programmer I have coded a lot together with Physicists who changed
careers. My wife is a Physicists and she, as all of her friends from study
times, work as teachers. One fried of mine after working a few years at SLAC
went beyond and studied theology, he is now a bible teacher.

------
dhimes
Ph.D. in Condensed Matter (surface) physics 1992, the "era of the 40 year old
post-docs." I had a talent for teaching, so rather than pursue the endless
post-doc trajectory I followed my talent and taught.

Now I work on educational software trying to extend my reach.

------
eanzenberg
Physicist phd(2012) and postdoc(2014). I went into data science(applied) after
my postdoc because I wanted to stay in the bay area. I still read papers here
and there and subscribe to journals like nature to keep the research side
somewhat active.

------
FiatLuxDave
Applied physicist here. Did my tour of duty in fusion. Been working in the
medical physics industry for quite a while now as my perpetual 'day-job'. On
the side I work on superresolution communication and various energy ideas.

------
zeroDivisible
I'd love this to be a series of monthly posts, asking for updates for people
with certain professions or backgrounds.

There's so much which I don't know about everything outside my field of work
(and inside too, I guess).

------
mef51
PHD student... working on measuring the magnetic field in clouds that are
really really far away! Astronomically far away! This is to understand and
observe star formation.

I don't see myself staying in academia though I love research.

~~~
SiempreViernes
Yo! Zeeman splitting or faraday rotation?

~~~
mef51
Fancier! Goldreich-Kylafis of molecular lines (which you can do with weakly
zeeman splitting molecules) and anisotropic resonant scattering (houde et al.
2013, 2014 etc.)

~~~
SiempreViernes
That does sound fancy indeed

------
billfruit
To my layman's eyes Compressed Sensing looked to me fundamental new tool to
look at things, did it really make any difference to the methods of physics?
Did it have any effect in electodynamics or optics?

~~~
gsmecher
This is a great question but it's a little off-topic.

I've been watching compressed sensing (CS) from the sidelines for the past
decade, while wearing experimental-cosmology, synthetic-aperture RADAR, and
instrumentation/FPGA hats. (Not electrodynamics or optics, per your question,
but nearby.) Here's my perspective.

If you're doing compressed sensing correctly, it transforms the entire
instrument from electrically complex and algorithmically simple, to
algorithmically complex and electrically simple. Unfortunately this isn't a
strategy you can bolt on to a traditional architecture and turn off as a de-
risking strategy if it doesn't work. From the perspective of a team building a
tool to accomplish a task, it's an all-or-nothing gamble. Consequently, the
challenges to aggressive adoption of CS are both technical (can it work?) and
programmatic (is it feasible given human, political, and financial
constraints?)

In the fields I've been exposed to, state-of-the-art instrumentation is
complex enough that domain experts spend their entire careers understanding
the quirks of a few established instrument topologies. Where CS is applicable,
it would take a big leap of faith from an entire team to build an instrument
around CS. Before that leap of faith is accessible, the team would have to be
conversant in CS research and the implementation of CS algorithms in practice.
And, after the leap of faith, a successful CS project requires secondary leaps
of faith from funding agencies to get these instruments built. These barriers
are highest for exactly the kind of complex, expensive projects where CS is
supposed to shine.

As an aside, the latest IEEE Signal Processing magazine
([https://ieeexplore.ieee.org/document/8653526](https://ieeexplore.ieee.org/document/8653526))
has an interesting article on hardware architectures for compressed sensing.
As CS progresses, and as CS researchers transition from pure-CS research to
applied-CS techniques, the use of CS in physics will probably grow.

~~~
billfruit
So I gather that CS requires new types of instrument typologies and know-how,
till we see it deployed more widely.

I am though ever in awe of the hype that ML/NNs are getting for the past 2-3
years, where as Compressed-Sensing is likely to be a more fundamental shift in
many fields.

~~~
ChrisFoster
I'd argue the opposite.

Vanilla CS theory assumes a very simple system model for the measurement
process (ie, linear measurement) and shows that with a few assumptions you
need far fewer measurements than you might expect. This by itself is a
valuable observation.

However, I'm less impressed with CS as a practical tool because in many
realistic systems the measurement process is nonlinear and difficult to model.
That is, the desired system state you're trying to infer is some nonlinear
function of the experimentally measured quantities.

I think the best instruments of the future will use CS ideas to _motivate_ the
use of an appropriate sensor domain to reduce the number of measurements
required. But that actual computational reconstructions of system state (ie,
inference) will be done with a nonlinear differentiable model which can be
optimized using a data driven approach and with ML-like tools.

In addition to the benefit of allowing nonlinearities, this approach gives you
a lot of freedom to optimize the computational complexity of the
reconstruction (ie, complexity of the system model) vs accuracy of the
results.

------
mikekij
Physicist (MS, not PhD) here. Started a healthcare software company and went
through YC. The problem solving strategies learned in my physics training have
been incredibly helpful in starting a business.

------
grigjd3
I worked in numrel before and was even offered a tenure track job focused on
teaching. I didn't want to work 70 hour weeks though. Now I focus on my family
and data pipelines seem like a nice hobby.

------
chicob
MSc in Physics (Plasma Physics diagnostics). I am currently a farmer, but I
hope someday I can get back to developing electronics, hopefully something
related to farming and/or meteorology.

------
paulvs
I found Susan Fowler's story quite interesting, as told on her blog:
[https://www.susanjfowler.com](https://www.susanjfowler.com)

------
georgeburdell
Condensed Matter PhD 5 years ago. Now a (once or twice promoted) hardware
engineer at one of the big Silicon Valley hardware companies. Job occasionally
uses my PhD, but not often

------
lmtsui
PhD 2018 in physics, condensed matter theory. Currently a postdoc at MIT.
Exploring life outside of academia for the past year, did some RL projects for
fun on the side.

------
mark_l_watson
I gave a degree in Physics but I have been doing machine learning since the
1980s (and some distributed systems and NLP).

Physics is a great general purpose education.

------
philshem
PhD 2008, now a data "science" generalist

------
gaze
I’m in my last year of a PhD in quantum computing, working on experimental
error corrected entanglement between superconducting qubits.

------
return0
Switched to neuroscience, much more low hanging fruit there. Though imho work
in academia has an expiration date.

------
konschubert
Growing the engineering team. I jumped ship one year into my PhD and joined an
early stage startup.

------
tdsamardzhiev
I work as a game programmer for AAA studio. Most of our engine folks are
physicists by education.

------
stared
Deep learning consulting ("I teach people to teach machines").

Finished my PhD in quantum optics in 2014, but immediately moved to data
science.

Why not physics? Full version: [http://p.migdal.pl/2015/12/14/sci-to-data-
sci.html](http://p.migdal.pl/2015/12/14/sci-to-data-sci.html)

tl:dr: I wanted a fast-pasted field with more freedom. Physics is now stale
(no fundamental changes in the last decades, compared to each year in deep
learning; cf. physics in 1900-1920), and academia offers a rigid framework of
grants and feudal dependence. In data science, as a freelancer/consultant, I
get much more freedom. Even in companies, one is able to migrate in a matter
of weeks, not years.

Money was a nice perk, not anywhere near to the main motivation.

~~~
n4r9
Hey, thanks for the link. I've had a vaguely similar path (quantum info PhD ->
short software stint -> lectureship -> software/data job). Your list of
benefits really resonated with me.

For myself at least I would add job security as another benefit. I struggled
in vain for 6-7 months during my lectureship applying to any and all postdocs
I could find, so the prospect of cycling through 2-3 year postdocs in various
locations eventually overwhelmed me. I loved the research but realised I
didn't love it enough to potentially live apart from my partner and regularly
go through months-long application processes.

On the flip side, for me freedom is actually something I miss from academia.
Admittedly that's partly because I'm a regular employee rather than a
freelancer. Outside of teaching, my time was basically my own and I was free
to work from home or anywhere else. I'm somewhat resentful of having to
justify what I'm spending my time on day by day. Not that my manager would
particularly mind if I spent a day researching something slightly blue-sky,
but the fact that I might have to respond to questions about it creates this
background tinge of anxiety that I find prevents deep creative thought.

------
gdevenyi
Neuroscience

------
hpcjoe
Me[1]: Ph.D. Theoretical/Computational Physics in mid 90s[2], working on
simulation of semiconductor defect states, formation and migration energies.

Went commercial rather than academic, as the job market was insanely crowded,
and I didn't have "enough" differentiation in my opinion, to land a tenure
track, at a lower tier school given the huge influx of high quality talent
from the former soviet union (FSU). I was around during the whole Young
Scientists Network days (early 90s) where we collectively did deep soul
searching on whether or not Physics as an academic career actually made sense.

I joined a supercomputer maker in 1995 ABD, and finished writing up (my third
rewrite, first was in 1994, final accepted one was in 1997) and defending . I
stayed with them for 6 years, until I saw that they had no real hope of long
term survival.

During my time in school, I'd been a consumer of Supercomputing systems across
the US, and in my department. I decided that was the career direction I'd go,
with the idea that I'd become an entrepreneur after watching successful
companies develop and grow. Learn from them, not just their successes, but
their failures.

Needless to say, my first job I learned a great deal. My thesis advisor was
still trying to push me to postdocs with her former classmates, and I was
tempted, but my wife and I decided to start a family, and that kind of nuked
that direction.

I left the first place, and was recruited to help another company bootstrap an
HPC division. That was fun, I got experience in all the non-technical side of
businesses as well as the tech.

They had a financial crisis, and I took a small package and started my own HPC
company. I ran that for 14.5 years. It was a wild ride, and I learned a
tremendous amount (e.g. I failed in many non-fatal ways). Unfortunately, the
last learning experience was in fact, fatal. I joined a cloud company and have
been helping to build a "next generation" cloud.

My thesis advisor just retired, and she's been sending me things about
Astronomy and Physics openings. They are mostly adjunct teaching things. I
love teaching, I get a real blast out of it. But the adjunct life is a massive
pay cut, at a time I cannot afford one. I don't have enough spare time to do a
good job either.

Recently, my alma mater has an opening in the CS department for HPC-like
people, which is definitely up my ally. I've got a real interest in ML and its
connections to statistical mechanics, quantum computing, numerical
simulations, etc. But I am undecided as to whether or not I should look into
this more.

[1] [https://scalability.org/about-me/](https://scalability.org/about-me/)

[2]
[https://academictree.org/physics/tree.php?pid=743767](https://academictree.org/physics/tree.php?pid=743767)

------
olooney
I earned a masters in physics about 15 years ago and started in industry as a
software developer. Over time I started working on more analytic applications,
basically because I could understand the math, and for the last six years I've
worked full time as a data scientist. My physics background has been a huge
help:

* Linear algebra: stuff like the QR decomposition for solving the "normal equation" of least squares regression, eigenvectors for PCA and the singular value decomposition for T-SVD, and so on. Linear algebra shows up everywhere in applied mathematics, statistical modeling, and data science. It's actually relatively simple and can be understood completely in about two semesters, but physicists do get a lot of practical experience and intuition. For example, eigenvalues turn out to be very important in quantum mechanics, so I suspect physics students spent a lot more time thinking about them compared to almost any other major.

* Vector calculus, matrix calculus, and optimization: physicists see this stuff in classical mechanics, E&M, etc. We can easily visualize vector fields and know a ton of relevant theorems and notation. We learn specific techniques such as the method of Lagrange multipliers for solving constrained systems. All of this helps because a huge chunk of statistical modeling and machine learning is formulated as an optimization problem, often a constrained optimization problem. For a statistician studying, say, SVMs for the first time, things like Lagrange multipliers and KKT conditions seem to come out of nowhere, while a physicist would have seen them in several other contexts first.

* Scientific programming: Most physics undergrads will have at least some experience with numerical optimization or simulation. This is a little different than application development or implementing an algorithm. The main trick is to be able to translate equations into performant, vectorized implementations. You also need to understand lots of practical things like rate of convergence, condition number, singular gradients, etc., just to be able to debug when things aren't working correctly.

* Experimentation: machine learning is basically empirical. Cross validation is essentially designing, conducting, and analyzing experiments. We improve our models not by proving theorems, but by forming and testing hypotheses. Basic stuff but extremely helpful in practice.

You have to remember that 10 years ago you couldn't really get a degree in
machine learning or data science. (I believe degree programs and
specializations with those names now exist and becoming fairly common,
although I've had mixed results when interviewing graduates of such programs.)
I would say the physics joins statistics and applied mathematics on the short
list of degrees that, purely by chance, covered most of the relevant material.
Of these, physics is probably the worst choice if you know you're going in
that direction, because a physics degree only provides a thorough grounding in
the prerequisites and little to none of the details. I had to learn much of
that on my own through self-study.

------
ritoune
ML

------
andbberger
machine learning

------
bmperrea
stablecoins

------
notthemessiah
Nothing, as they don't seem to be hiring physicists.

