Ask HN: Physicists of HN, what are you working on these days?

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.

(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.

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.

(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 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 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.

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.

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.

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 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.

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.

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.

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.

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

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

https://royalsocietypublishing.org/doi/full/10.1098/rsta.201...

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

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

http://britneyspears.ac/physics/about/about.htm

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.

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

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?

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.

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.

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.

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 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.

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.

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...

(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.

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.

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

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

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

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

(PhD, 2005 in condensed matter)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Now my job title is "data scientist".

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

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

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

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

PhD in 2012, Theoretical high energy particle physics.

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

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

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

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

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

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

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

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

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

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

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

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

(HEP Phd, finishing)

I'll also be joining the data science world.

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

Seems like a good moment to take some time out

(PhD, 2008 in Complex Systems)

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

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

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

You can look at the results here: https://intelactica.com

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I found Susan Fowler's story quite interesting, as told on her blog: https://www.susanjfowler.com

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

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

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

Physics is a great general purpose education.

PhD 2008, now a data "science" generalist

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

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

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

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

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

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

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

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

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

Neuroscience

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ML

machine learning

stablecoins

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