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Ask HN: Physicists of HN, what are you working on these days?
456 points by sachin18590 on March 27, 2019 | hide | past | favorite | 316 comments
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. 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?

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.

To be entirely fair, people not educated in physics would likely have a very hard time at your job because we don't know the terminology or what makes sense for your field. If I tried to do what you do, I'd likely have no clue what half of the pigeon-scratch is because I flat out do not even know what the words or concepts are.

That's a fair point.

Still, I feel I could take any of my students and in one month have them up to speed on what to look for and what things mean.

And I don't mean to suggest that finding challenging work is any sort of failing of my employer. I have been able to diversify the position to include doing graphics in R and some stats things and, along with some other people, I think we can sell some sexy Bayesian things.

They are very receptive of new ideas, but... it is not a growing industry (thankfully), so any sort of advance is going to be a jump to a different field. Which is possible, and I'm working on it.

My point primarily is when you read these things about people doing all sorts of wonderful exciting things, you get the impression that what you have done or are doing is crap. Perhaps it's nice to hear that a lot of people do not set the world on fire. Takes a little pressure off.

I think you could swing some academic or national lab position if you put some time into it.

Is there anything even tangentially related to your dayjob that you could mine for ideas? Or maybe take some time to come up with some research direction?

A places like Oak Ridge could have something interesting available.

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

Pick up a copy of Automate the Boring Stuff with Python.

>As an undergrad I was excited about chaos and nonlinear dynamics.

I, too, read the Strogatz book :^).

:) That's a good one, Devaney was more my era!

Devaney was my advisor's advisor when he did he his PhD at BU. I took a course on discrete dynamics and chaos and wrote my senior year thesis on bifurcation diagrams.

We used this textbook by Devaney: https://www.amazon.com/First-Course-Chaotic-Dynamical-System...

Was absolutely fascinated by the class. One of my favorites from undergrad.

I had his Chaotic Dynamical Systems book checked out from the library for years and carried it everywhere. I should find a copy, what a great book!

I wonder if there's a good sort of "postmortem" on what happened to NLD and Chaos. It seems like everyone said, "Ok, that's a thing and it explains a lot but we can't do anything with it."

Nonlinear systems by Hassan K. Khalil (ISBN 978-0130673893) is also worth it to solidify the underlying maths!

Sorry for the aside, but... Haskell gang represent!


As a further aside, I do get to do some fun graphics in R and I'm pretty impressed how R has become the statistical standard and graphics too, mainly I think from one super dynamic statistician.

Now that R is entrenched I can work on moving everyone into Haskell!

> I can work on moving everyone into Haskell!

So pure and innocent. I've found that programmers are not too fond of having even a simple IO operation lead to discussions of category theory. Good luck in your noble lambda crusade :-). The future is functional! Hack the type systems!

Would you have any interest in doing Haskell full-time? I know several well-funded companies looking for folks with similar backgrounds to yours, in SF, NYC, and London. Happy to connect you if you’re looking for something more interesting and possibly better pay.

Sorry to hijack the OP, but I am interested in companies in NYC doing Haskell. Would you mind sharing?

Sure, the two I had in mind in NYC are Kadena (http://kadena.io) and Jane St. (https://www.janestreet.com). Jane St. is more an OCaml shop but they like Haskell people. Kadena builds everything in Haskell.

Thanks. I know of these. I will apply.

* "All, in all"

Just a hunch - are you in the southeast US?

Northeast, although I do have to travel to the SE now and then.

"""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?

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.

    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.

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

Sounds like a plot for a really decent sci-fi book with an AI as the antagonist.

He can use it such that the model's predictions are still manually verified by him before actually submitting them or whatever. Seems pretty harmless to me.

In his current situation, if he zones out, he produces nothing. In the situation where he's aided by the potentially faulty described system, if he zones out, he produces erroneously transcribed data.

What? In his current situation, if he zones out, he can produce erroneously transcribed data too. What's your point?

What? In his current situation, even if he doesn't zone out, he can still produce erroneously transcribed data too. Why even go to work?

Manual verification is likely just as tedious as doing it by hand in the first place.

Probably. But maybe he'll get a kick out of modeling and automating it. Challenging his intellect seems to be the key point here.

I'd first go for a pareto approach. There's probably some easy automation that can aid the manual work, which is easy to implement and provides visible performance gains. Stuff like pre selecting interesting image parts (aim for high false positive rates and zero false negatives) or even just an interface for speeding up dull manual work like pulling documents from the repository, associating relevant documents and presenting them in a fast acting UI.

I've done something similar, automating a similar process. Essentially the screen was split on half. The left was the actual image, the right was the data of what the program thinks it is after OCR and some magic interpretation. The user can scroll around and both panes would move together. User clicks accepts on each value. Otherwise user can edit each value, or add a new value (if it was completely missed) and then accept. Once everything is accepted they can save the data.

On paper it saved a lot of man hours but it was never fully rolled out. The project didn't have a long lifespan and we had enough people sitting around doing nothing to just manually do all the work. But I got a nice award for it LOL.

one could make a similar argument that no one should have tried making a car that could drive better than a human

This is more like some guy in the early 1900s having the job of hand-delivering important information on a horse.

Then deciding on his own on the advice of HN that he should try a car for that task instead, the car breaking down, and the guy getting fired because his employer never approved of this whole "car" thing and he shouldn't have introduced that into his job workflow without having talked to his employer about it.

I will take a look at it, thanks. I have tried some off the shelf OCR things and had little success. But what I do is really needle in the haystack kind of thing. I am looking for a "U" or and "Sr" in a particular place and happy to find one in a thousand pdf pages.

I am looking into auto-scrolling pdfs and batch loading, sequencing of pdfs to make things easier. But I will definitely check out fast.ai - thanks!

Only problem is I don't know how likely it would be that the writing is neatly separated by character for mnist to work, and handwriting recognition isn't accurate enough. Maybe some restraints on the inputs will fix that

Regretfully pragmatic viewpoint here: The poster is currently relying on this menial job for income. Automating it before he has something else lined up could be a really bad idea, financially.

This is a great idea. If you manage to pull this off you'd gain extremely valuable experience that would allow you to easily transition later on.

(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

[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.

> 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

- 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.

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-...

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

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

Any chance you'd be interested in talking to me about a device I am working on and am trying to evaluate? Your knowledge of accelerator physics would probably give you good insights about problems it may have.

I was recently reading about single pixel cameras and went further deep into UED cameras. The promise of what potentials they can unlock is amazing.

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.

> We test [...] Newton's inverse square law

Imagine taking Newton down in the Replication Crisis Wars!

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.


Newton would not be taken down by this experiment because Newton's laws have a known precision bound. If we did not include the precision bound in our holistic idea of "Newton's laws," they would already have been falsified by relativity and quantum mechanics. Every physical theory has a precision bound that is determined by the most precise experiment that has ever tested it.

Apologies for the delayed reply -- HN's noprocrast setting is a powerful taskmaster.

What we do is search for deviations from Newton's inverse square law. Depending upon the distance over which an experiment is performed, the ISL can be correct to a precision as high as ~10^-10 [1]. In particular, our group works to test whether the inverse-square law (or indeed gravity at all) happens as predicted at distances shorter than 100 microns [2]. It still amazes me that at distances shorter than half the diameter of a human hair, nobody has any idea whether gravity even happens.

Newton's early observations (and Kepler's) have been replicated many times -- there is no risk of a major replication-crisis upheaval there.

[1] https://arxiv.org/abs/hep-ph/0307284 (Figure 4)

[2] https://arxiv.org/abs/hep-ph/0611184

How about the going the other way? I'm an engineer (hydrodynamics and optimization, and econometrics way back before all that.. and.. aerospace before that) who just finished a PhD and who has been writing software professionally for a large-ish engineering-software-company for 5 years while working on said degree. I have a serious-for-an-amateur physics side habit and really want to bring my work and play closer together. At work I look after a code base that is a half million lines of ancient f77/c++ so maybe that is a factor in my wanting something new!

Any room for scientific software developer at LIGO? Gravity is a beast... I will leave it at that. But I can signal process and I am not afraid to ask stupid questions. Looking for growth industries, I've had my eye on quantum computing and am just putting resumes together now. I'm really into differential geometry and topology but my understanding is amateurish and what I've learned of QFT, geometric physics, topology, etc., is uneven at best.

If you really are interested in software dev / data science I can ask around my company. They have a Seattle office* and a broad range of needs for different developer backgrounds --- at least when all offices are considered. Typically there is wiggle room to work from one office with a group based elsewhere. I can't guarantee anything though -- it's been hard to get anyone hired lately!

Back to gravity and such: I doubt you find anything, without physics in it, that draws you in as much as your present work, but it can't hurt to look around! How about a switch to focus on quantum computing if data science doesn't draw you? Bar that, what about numerical simulation?

*I'm not in Seattle.

It is much more challenging to go the other way -- there are a lot of people deeply in love with the field, with field-specific training, who would like to stay in (at significant financial and temporal sacrifice).

That said, if one wanted to move toward LIGO from a software background, the chink in the armor is that the signal-processing is hard and computationally bound. Helping signal-analysis groups to find efficiencies there would have an impact.

I continue to keep an eye on quantum-computing infrastructure hires in the Seattle area -- so far, I've proven to be too physics-focused to want to work solely on provisioning the (awesome) cryostats that house the quantum-computing devices under test. That may prove to be a sub-optimal life-decision -- time will tell :).

Fair enough. And yep, I plan to find a chink in the ol' armor somewhere. Just get me 20% time thinking physics at work and I will be happy... (even if it we never "get there" with the device) Or more likely my insatiable appetite will only increase..

I did undergrad at UW, several of my classmates did grad work on LIGO and were on the gravity wave teams when they had their first detection. Amazing what timing can do for a career. I visited Hanford LIGO in 2012 before the detection, really interesting back then to hear their frustrations and steady commitment.

I graduated and did a few strtuos, now working on autonomous self-delivering ebikes in SODO.

(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.

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

Oh wow! This looks like the physics book that I always wanted to read. Phd 2016 in condensed matter physics - now working in a mid-size (~600 people) software company as a data scientist.

Modern Classical Physics is a great book - I've recently started working through it (just about to move on to chapter 2). I'd be interested in chatting about it with others/cross-checking solutions. Anyone who's interested, drop me an email (address in my profile).

If you're thinking of getting it but want to check it out, there's a 2012 draft version that the authors have previously taught from here: http://www.pmaweb.caltech.edu/Courses/ph136/yr2012/. It's not the same as the book, of course, but from a skim it seems quite similar.

Thanks! This looks like what I will start with. Can't find your email in your profile, though.

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.

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

Does Slack let you write LaTeX?

And is this book a good read for someone re-learning math having only taken high school physics?

Depends on what the math re-learning covers. You'd need differential equations and 2nd year calculus.

A good choice for less math might be "Gravity from the ground up" by Schutz.

I know what you mean on keeping the physics part alive. I've written a bunch of code for doing simple molecular dynamics and varying the interaction types. I've also gone off and discovered I like number theory.

None of this pays the bills, right now I help build clouds, and I used to build supercomputers, and high performance storage systems.


Somehow I doubt it! In any case, you are like me - doing engineering and software, while doing my physics as a hobby and loving it. I'm working on Physics from Symmetry. Definitely a 21st century book - not at all like the traditional development of the subject.

Physics from symmetry looks really fun. Another book for the pile! (see https://en.wikipedia.org/wiki/Tsundoku)

Love your website, when I get home I am going to boot up my PC and play around with some of your code :)

I was actually at Pitt when Rovelli and Newman were still team teaching GR and always regretted not taking this course. Would Modern Classical Physics be enough in the GR section to get the basics down? It looks decent for the stuff I know about (optics and so on).

I’d imagine there could be some really interestingly massive simulation possibilities integrating nbody with unity’s new ECS system

Mind reader!

Yeah, I'm keen to give that a go. Interestingly it is not a problem that is very easy to parallelize, since every mass will affect every other mass. There may still be some cool ways to use ECS though.

Yeah I can see that... at least all the other non-rigidbody stuff is (theoretically) much more efficient.

Have you ever considered working on some low-hanging fruit problem in a non-physics field?

Some of the Unity asset work might fit that description e.g. provide a library of chaotic attractors packaged for use in a game.

My grad work had no connection to data/stats and I have not bumped into any low hanging fruit where Riemannian geometry might be the answer!

I don't know that differential geometry ever made anything simple, so I don't know if there is any obvious fruit to be had. But there might be some, weird, unexpected fruit in the back; for instance, there exists a formulation of thermodynamics in terms of differential geometry: https://ui.adsabs.harvard.edu/#abs/arXiv:physics%2F0604164

As far as I know, a good differential-geometric understanding of nonequilibrium thermodynamics still hasn't been achieved.

The central issue is understanding how changes in control parameters (for instance concentrations of catalysts in a chemical system, or local fields in a spin system) affect the evolution of the probability distribution over states. Some work has been done in close to steady state (for instance [1,2,3]) but it's far from resolved.

This has some nice applications - designing efficient protocols for microscale devices, for instance.

[1] https://arxiv.org/abs/1603.07758 [2] https://arxiv.org/abs/1507.06269v1 [3] https://arxiv.org/abs/1201.4166

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.

> I run the team that builds software for Google's quantum computers

Is there any significant difference to payscale and hierarchical autonomy in these non AI research teams? I am assuming distributed privacy preserving ML team (is this team close to federated learning one?) also falls under more non AI research teams right?


> my background in quantum computing caught back up to me,

I would be curious on that part. Could you describe the process what happened there? Internal skill screening program? Did you jump projects?


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?

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.

Awesome, do you sneak in experiments on time crystals ;)

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.

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.

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.

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.

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...).

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.

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.

So what happens to the seed??

Don't quote me on this because I only covered the topic briefly in some applied classes before doing 6 years of research, but if I remember correctly, you grab it with long tweezers and dump it in a shielded "garbage can" type container. And fill tons of paperwork that involves estimating the radiation dose delivered to everyone that could've been exposed. And probably present a post mortem at conferences about how you dealt with it.

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.

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.

> 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

Sounds like engineering and SE, not physics

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.

> 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..?)

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


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.

I read the comment as saying that the postdoc kept his reservations about power law distributions to himself until after he had tenure, so as to not upset the applecart I suppose... ?

I read the comment as implying that the colleague had published a paper on power law distributions in order to publish (pressure) while the poster knew that the colleague did not believe in the subject.

There was a similar issue with power laws in biology, mostly driven by a guy named Barabasi. I don't think any of his results have held up when someone did the math right, but he's gone so far as to try to politically pressure people to keep quiet about it because they were hurting his career.

I've been out for a few years, so I don't know what the current state of affairs is.

Surprised to hear this about Barabasi, I'm no physicist but enjoyed reading his books on network science as a layperson.

Is there any published criticism against his work? His papers get cited everywhere, I know citations are more metric of popularity than scientific quality but I find it puzzling that his work continues to get attention if the results don't hold up.

You could start with Pachter's sendup (https://liorpachter.wordpress.com/2014/02/10/the-network-non...), and the Wikipedia article on Barabasi has a short section on criticisms. I read his work, figured out it was nonsense, and put him on the list of authors whose papers I automatically threw in the garbage. Kim Lewis is another one of those.

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.

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.

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.

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.

I bet hedge fund pays significantly more too, no?

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

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.

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.

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.

> 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.

Or if you live in a country that grants permanent positions early and easily.

I think many places have seen this problem and are actively trying to solve it. I know several spousal hires.

I know a few people who have stayed in academia and done this.

In my case, my partner is not in academia which makes it a bit easier.

> Latest purchase was Exercises for the Feynman lectures.

Those are wonderful! I dug them up late in my undergrad and spend some fun times with them.

That's great! I did not know that the exercises appeared in book form until I found it a used book store. Read through most of the lectures in undergrad/grad school but needs a revisit.

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.

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!

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."


>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.

This makes me so incredibly depressed.

Nice quote. I'll have to read that paper later.

You can merge ML and theory in at least one way. I attended a talk by Prof. Karen Willcox of the University of Texas at Austin (I'm a PhD student in mechanical engineering there) where she argued that in fluid dynamics and combustion at least, it's better to use "model order reduction" instead of machine learning. The problem with many models (e.g., Navier-Stokes equations) in these fields is that they are computationally expensive. Model order reduction looks for ways to reduce the computational cost of the model while maintaining accuracy, and it uses many of the same techniques as machine learning. Based on the examples she gave it seemed to be the closest thing I've seen to merge the two.



> For now, however, in hard-core physical science at least, there is little evidence of any major BD-driven breakthrough

> 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

The thing is, I've just spent two years working for molecular neurobiologists in the field of Single Cell RNA Sequencing, and large data collection has definitely lead through tons of breakthroughs there.

We can now classify cell types based on gene activation, on top of the previously existing morphology and location the cells are found. That can then be used to discover new subtypes, the origins of cells during embryonic development, and even predict which cells will evolve into others[0][1][2][3]. All of this requires vast amounts of data to ensure there is enough statistical power. In fact, the insistence on using unbiased samples before applying clustering algorithms is a big part of overcoming biases based on pre-existing expectations.

(Also, may I request that you edit your comment and break up that block of text into sub-paragraphs, for the sake of readability?)

[0] http://mousebrain.org/

[1] https://linnarssonlab.org/osmFISH/

[2] http://gioelelamanno.com/post/velocitynature/

[3] https://www.nature.com/articles/d41586-018-05882-8

> 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.

This is so sad

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.

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

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.

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

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

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

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

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.

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

What's an ISV?

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".

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".

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.

> 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.

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.

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.

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.

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.

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.

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).

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.

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.

there is a long and distinguished history of physicists in biology (w some nobel winners). but ultimately, i wonder if a physicist could do xray crystallogrphy on the microbial fossils found on mars to determine their genetic basis. (dna fossils?)

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.

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.

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.

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?

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.

Thanks for the reply!

Anecdote: One of my most enjoyable SW jobs ever was working for a while as a staff project architect/programmer for a bioinformatics lab at the University of Washington. Great meaningful project, lots of freedom, smart colleagues. BTW I have a physics and life science (medicine) background, but it was partly coincidental, and an interested non-scientist could have done it and learned a lot. FWIW it did not pay exceptionally well, but it was reasonable salary at the time and the environment was really enjoyable and productive. This was a few years ago, but I would imagine given the current labor market there would still be significant need for academic staff SW positions. Good luck and post back if you do make a switch! As this thread indicates, this is a topic of interest to many.

Edited: Re-reading batbomb's reply, I also remembered a friend who is currently a data scientist for a venture-incubator, but was previously at a national lab. Many of the projects he described were very cool, and challenging.

Some of us like ROOT :)

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.

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

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.

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.

I don't think you're necessarily wrong re: difficulty getting prestigious post docs after graduating from a young lab in a newer field, but I think you're way overselling it's importance.

The fundamental issue is the field is not growing (very much). Each professor will graduate 20-50 students over their career, but only one will get their job when they retire (on average).

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.

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.

And to answer the question, (MS 2016 Condensed Matter Theory), Did risk assessment for two years, now I simulate systems of systems.

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.

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.

>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).

> But even 20 years ago, the guidance was to look for jobs in finance or computers.

Yeah. I keep kicking myself for not taking the Wall Street guys who called me more seriously. That would be one important message I'd like to send backward in time.

I remember some of the Physics departments I knew well commenting that they were getting 250+ applications for each open tenure track position (early/mid 90s). It skyrocketed to 1000+ at one point for a few places.

Based on some back of the envelope math, and a realization that a fresh Ph.D. with ~6 publications (2 PRL, 4 others) would not be a good competitor to a senior FSU physicist with 50+ publications to their name, a solid reputation, and an interest in moving out of the FSU quickly.

I determined that market forces had flooded the supply side of physics with, well, extremely good candidates for the same positions I was applying for. And my ability to compete with them was low based upon the main components that hiring committees cared about.

So I looked elsewhere. 20+ years later, I've made a good career working in computing, but I really do miss physics and research in general.

A surprising number of physics students I studied with, were also mountain climbers. They seem to be very much, 'because its there' sort of people.

This kind of reminds me of what Jeff Hawkins once claimed was the issue with making sense of how the brain works:

> So why don't we have a good theory of brains? People have been working on it for 100 years. Let's first take a look at what normal science looks like. This is normal science. Normal science is a nice balance between theory and experimentalists. The theorist guy says, "I think this is what's going on," the experimentalist says, "You're wrong." It goes back and forth, this works in physics, this in geology.

> But if this is normal science, what does neuroscience look like? This is what neuroscience looks like. We have this mountain of data, which is anatomy, physiology and behavior. You can't imagine how much detail we know about brains. There were 28,000 people who went to the neuroscience conference this year, and every one of them is doing research in brains. A lot of data, but no theory. There's a little wimpy box on top there.

Not that physics has no theories, but I dropped out of studying physics myself over a decade ago, and at that time it felt a lot like the balance in physics has shifted towards having to measure and process disproportionate amounts of data with so much precision that it has to be automated, or like you said do a ton of really complicated modelling. It feels a bit "stuck" that way.

[0] https://www.ted.com/talks/jeff_hawkins_on_how_brain_science_...

I think you're seeing this in physics because, in terms of experimental accessibility, the low-hanging fruit are mostly picked.

Most parts of the Standard Model are verified to 5σ at all energy levels that are accessible to us in everyday life and in contemporary particle accelerators, so it's increasingly unlikely that we've missed any obvious mechanisms.

If we're going to find some completely new physics (in the same way that quantum theory and relativity theory were completely new at the turn of the 20th century), it's probably going to be at energy levels that contemporary particle accelerators cannot yet explore.

EDIT: That's not to say that we know everything that is to know about the accessible energy levels. There's so much in our theory that's yet unexplained, e.g. for all I know, we still don't have any idea why time works the way it does.

> Not that physics has no theories, but I dropped out of studying physics myself over a decade ago, and at that time it felt a lot like the balance in physics has shifted towards having to measure and process disproportionate amounts of data with so much precision that has to be automated, or like you said do a ton of really complicated modelling. It feels a bit "stuck" that way.

The reason for is just how damn good all the Physics Theory is. People keep having to look closer and closer to try to find a place, any place, where the theory "fails" (in a sufficiently spectacular fashion) hoping that that might point the way to new phenomena and new theory. There's a giant multibillion dollar hole on the ground in Switzerland who's main purpose was to find a chink in the armor of HEP or help point the way to new theories. It failed spectacularly, but they're already talking about building a bigger one :)

> The reason for is just how damn good all the Physics Theory is.

> (majewski's comment below) I think you're seeing this in physics because, in terms of experimental accessibility, the low-hanging fruit are mostly picked.

I mean, yes and no. The Standard Model is amazing. But I think we're also kind of focused too much on the Big Questions, and in the arena of the not-so-big questions there are still a ton of gaps and missing insights.

Perhaps in the not-so-big questions, most of the low-hanging fruit may appear to be picked as well, but I suspect that is more a kind of myopia that can be solved by collaborating outside of our field.

Whenever I see an article these days about physics that really excites me, it tends to be a story where one or more physicists got excited about a seemingly small question, or ate a bit of humble pie and joined forces with chemists, biologists or some other field, and or both, and that working it out turned out to bring far-reaching consequences and novel insights. Same with maths, really.

I'm aware this is completely personal of course, and that there is a likely bias in that this kind of research tends to be more accessible and thus more fun to read for me, but still.

> It failed spectacularly, but they're already talking about building a bigger one

Awww it isn't that bad. They found the Higgs and at least verified we need to look at higher energy levels to find anything new.

Is it really bad that most time is invested in simulations? Seems like a very potent low-hanging fruit (not to mention how many cool things could come out of it).

Depends on what you enjoy doing. I spent a lot of time doing magnetic simulations during the earlier period of my PhD and the reason for doing them isn't that the theory can't explain what will happen, but rather that we don't have the mathematical chops to actually calculate it, so we build little artificial worlds and try to take measurements the old fashion way.

This was certainly a lot fun, but you won't be getting any thing particularly revolutionary out of it. I remember my advisor at the time running a few dozen CPUs of our poor excuse for a cluster (built mostly by yours truly with off the shelf components) for the entire summer to add a couple of digits to the ground level energy estimate of a completely artificial magnetic model :)

On the bright side, it taught me a lot about programming, numerical simulation and optimization

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/

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.

> 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!

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.

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.

> eyesight has changed dramatically

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

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... :)

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.

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.

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.

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

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.


Thank you!

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

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.

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.

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

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). 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.

Dazzling managers by multiplying matrices together and calling it AI.

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.

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.


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.

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.

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.

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

do you categorically think working in SEO or fintech is a net negative for the world?

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

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.

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).

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).

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.

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!

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

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.

Man I wish I could work with W7-X data, such an amazing stellarator!

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.

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.

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?

> 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

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.

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.

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).

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?

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.

Are you happy with the path you took? Are you still happy with the time you spent studying physics?

Absolutely, but I had been programming before entering uni and got most joy out of programming related courses.

But I enjoyed my time as a physics student. Physics is an interesting field to study even if you don't end up working in it, in my opinion. :)

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.

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).

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.

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.

(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.

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

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.

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.

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.

Ha! I worked on hybridizer, imager, pipetting robot back in 2002. This was in Java (not my choice), if I did it again it would probably in Rust and Erlang.

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.

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"

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

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

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.

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.

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.

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. :)

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.

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.

What wavelength was the laser setup?

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.

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.

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.

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.

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.

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.

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.

(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...

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.

(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.

[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

> nightmare stories Salary/job finding wise?

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.

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.

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