Are the questions about the career path as a computational scientist appropriate here?

Question

Mods and friends

I am myself a PhD candidate, and I am now preparing and studying for my thesis which is about the 4D space-time finite element method for hyperbolic conservation law. Actually I am a bit confused and a little worried about my future. Here is a list of questions for becoming a computational scientist/numerical analyst I am most concerned with:

• What projects/topics we should choose for a career path we would like to have? Should we take a more utilitarian approach to choose relatively simple projects that could lead to some average quality publications? or should we tackle an important yet difficult question that has lots of application in physics/engineering but you would undergo a several-year studying process.

• What kinds of skills should we learn for future career? For the example of programming, the academia mostly adopts MATLAB/Mathematica, Python/C(FEnICS,METIS), Fortran(LAPACK), however, learning from the discussion with my friends in various industries, VBA/C++/C#/Perl etc are more popular due to their graphical UI compatibility. Which path should we choose?

• If we would like to pursue a position in the academia, like PostDocs, should we focus on more of a theoretical analysis aspect of scientific computing? or more of the computational results? How about the positions in some research facilities like national labs?

If the mods think only part of these questions can be asked in Scicomp.SE, I will modify and repost in the main forum.

Answer 1

I think if you're interested in academia-only advice, you could try a question slanted that way on the academia.SE. Questions about computational science careers here are legitimate as well.

Two points from your questions:

Regarding programming languages, you missed the elephant in the room, Java :) That said, the sort of non-technical company that hires PhDs (Amazon, Microsoft, Rackspace), is actually going to be more interested in your skills and accomplishments than any specific language you have worked with. Of course, if you only bring Fortran to the table, that might be an issue.

There are many opportunities to work in computational science that still involve the more engineering languages like Fortran, MATLAB and Python. The obvious ones are companies like Google (well, maybe not so much the Fortran thing), MathWorks, Boeing, and the DOE Laboratories, but also check out smaller shops like Enthought and Continuum Analytics.

Answer 2

TL;DR: Yes. For now, tag all careers questions with career-development (no matter what it is!) to keep the tags we have simple. As Aron said, academia.SE can also be an option. See a more detailed opinion below.

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I agree with Aron about academia.SE. I think this could be one case where cross-posting might be legitimate, but in order to cross-post, you must phrase the question substantially differently on each site, because each site has different scopes and different audiences. In other words, if you post on academia.SE, I expect the question to focus more on the academia aspects, whereas if you post here, I expect the question to focus more on the computational science aspects. My standard policy when I see any question cross-posted verbatim is to notify the other site, discuss scope, and then at least one question gets deleted.

In terms of our scope, I think career postings are on topic as long as they pertain to computational science. Other "academic" SE sites, like Mathematics, Physics, and Theoretical CS, also permit career questions, and I see no reason why we should decide differently.

Tagging these questions is the main foreseeable problem I can think of. Tags like soft-question are meta tags, and not useful. Similarly, I'd like to minimize the potential for fragmenting a career tag with things like career-development and other synonyms. It was a huge problem on Programmers.SE, as you can see on their Meta. Thankfully, Programmers.SE is a huge site, but we're still small (and growing!), so for now, I'd like to adopt the simple rule:

If your question is about careers, interviewing, hiring, searching for a job, what types of projects are better to tackle early in your career, or anything career-related, just use the career-development tag. Keep it simple; we can figure out how to subcategorize later when it actually becomes a problem.