Career advice -- What can I do with a Master's in Theoretical Physics and a Bachelor of Computer Science?

[Rouky]

Hi everyone,
What can I do with a master's in theoretical physics and a bachelor of computer science ? I am in Canada, I had to do a master's before a PhD. I just decided to do something else than physics afterwards. I had the opportunity to do a PhD in computer science, in medical imaging, but I didn't want to become a researcher in this field, and I was worried about my perspectives of employment... I will do an internship, programming GPU's with CUDA, which I'm very excited about. However most of the jobs related to HPC are in the USA. I would go without hesitation working there but since I do not have a PhD i feel that my chances are weak... What do you people think ?

 

[jedishrfu]

One field that is hot right now is Data Science. Your coursework would probably fit well into that space. You might need more statistics and perhaps numerical /machine learning experience.

 

[Rouky]

One field that is hot right now is Data Science. Your coursework would probably fit well into that space. You might need more statistics and perhaps numerical /machine learning experience.

I know that Data science and ML are really trendy theses days. I read this book "Pattern Recognition and Machine Learning" by C. Bishop in a graduate level ML course I took recently. However I do not have any "real-life" experience which is a big drawback ...

 

[jedishrfu]

It's trendy but it's not rocket science, you should be able to pick it up fairly fast. I know some people who have taken Data Science seminars and gotten jobs once they understood the terminology. They already had the math background and computer skills to do it.

Here's a dated IBM Redbook of Data Mining published in 2007 that is still somewhat relevant at least for the concepts and terminology:

http://www.redbooks.ibm.com/abstracts/sg247418.html?Open

and at Coursera:

https://www.coursera.org/browse/data-science?languages=en

 

[soniajessi]

The exciting profession right now is data science, the hottest buzzword in the technology world, which is a promising career path over the long run, especially when you expand the field to include positions such as research engineers and machine learning engineers. As reported by the US Bureau of Labor Statistics, by 2026 (five years from now), there will be 11.5 million jobs in analytics and data science.

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[Locrian]

I agree with jedishrfu that analytics is an area you can work your way into. These days the online seminars and courses have very little value by themselves, but I think they synergize very well with your PhD/BS - you've already shown you can do the really hard stuff, and the online courses would show you have a familiarity with some commonly used tools.

One thing to note is that the terminology has changed a lot in the past decade, and even in the past few years, due to segmentation in the work. Here are a few examples:

"Data Scientist" is increasingly used as a general term for analytics folks who are either product or project oriented. Their goal is to ensure that teams are making data driven decisions, and their success depends as much on their communication skills, influence, and leadership ability as their technical skills.

"Applied Research Scientists" are expected to be the more technical folks, usually having a phd. I'd think this type of work would be of interest to you and you should look into it. AR types are as likely to be operations research as they are predictive analytics in their focus, and they're typically building machine learning (ML) systems for internal or external clients.

"ML Engineers" focus more on the operationalization of systems, and tend to have a focus on software engineering and a strong understanding of ML applications as deployed in cloud (or other SaaS) environments.

Obviously not all companies use these terms this exact way, and certainly it's possible for someone to be wearing all three hats at smaller places, but I do think this usage is increasingly common.

I'm not sure that data science and related jobs will always have the level of pay they have now (comp at big name tech companies is kinda ridiculous), but I do believe it is a long term career that's here to stay.

 

[valenumr]

Simulation? Many challenging problems that require both skills.

 

[Astronuc]

Please note that member Rouky posted the OP on Aug 8, 2017, and the account is no longer active.

That said, computational physics/chemistry/materials science and data analysis are major areas of effort now, including AI and Machine Learning (ML). We use AI/ML in material science to develop models of materials and properties under a variety of conditions that span the intended operating environment.

 

[jedishrfu]

There are a lot of govt and private research labs which are actively working on projects requiring detailed knowledge of physics and using ML as a critical component.

Look for jobs in acoustics, oil exploration, volcanology, underwater acoustics, oceanography, medical imaging

 

[hutchphd]

In my often strange career (my PhD is in surface physics), the easiest money I ever made was assisting companies having production inconsistencies in high tech (often medical) equipment leading to product recalls.. This required me to understand the physics and trace back abnormalities using usually quite comprehensive production data. I love mining data and the PhD gave me the required imprimatur: good for me and good for the poor production guys who were tearing their hair out and needed refuge. I always figured it out and I always (with a few exceptions) made everyone else look good. It defines success for the consultant. I more than earned my fee, and learned continuously.
I point this out not (only) to brag, but to point out that much of what I did had no "job description" or required coursework. The only requirement was a reputation earned by knowing lots of stuff, the ability to do some pretty good data mining, and to work really hard when absolutely necessary. I could always find good prople to fill in the details I lacked.

So be excellent at everything you do but do not think you are limited only to the boxes you have checked.

 

[jedishrfu]

The job psychology aspect is a great hidden skill to cultivate. I heard a story once about a programmer making an interactive transactional system on a mainframe where it was expected to have a lot of clients using it concurrently.

The astute programmer added a timed delay into the system that got fined tuned as more and more people signed on. His thinking was that client expectation would be ruined if the system slowed down as more folks used it so he decided to manage their expectation with a timed delay.

Finally, one day, his manager appreciative of his hard work asked if he could get a bit more speed out of it. He said he'd look into it and tweeked his delay a bit more. The clients were happy, the manager was happy, and he got an all-expenses paid vacation to Hawaii (I made that part up).