> Do not grow complacent in school, ye numerical methods researchers! Write your stuff in C++. The job market awaits.
Could you elaborate a bit on this? I think I know what you mean, but I'd like to hear more if you don't mind.
Could you elaborate a bit on this? I think I know what you mean, but I'd like to hear more if you don't mind.
Not such a big deal if you are a pure-researcher in, say, CFD, and not such a big deal in a national lab, where the big codebase may be FORTRAN anyway (caps to denote old school code bases). But it really limits your mobility not to be "C++ first". Lastly, if you land a job at a big numerical analysis company, you may be looking after legacy products forever if you are "just a Fortran person".
Well, this is obviously anecdotal. Sorry to state such bald opinions. But it's what I've seen, and really would have been helpful to know as I was going through school. I wrote a lot of Fortran and Python. It's left me playing catch up and it could have been avoided with comparative ease compared to my time budget now.
I'd also give a quiet nod to trying to get familiar with modern dev-ops tooling. E.g. docker, kubernetes, and interacting with at least some forms of cloud storage. Get used to the idea that a bucket is not actually a directory, it's a hierarchy of prefixes and learn how to use that. Think of a dockerfile as learning how to write a makefile. It's worth learning the basics of, and it's not hard for simple things. HPC work has traditionally been a lot of fairly custom clusters (Condor, anyone?) or problems that are best solved with a lot of RAM on one machine. However, things are moving towards commodity solutions (i.e. "the cloud") or at the very least, common software layers. You don't need to understand the the ins and outs of managing a kubernetes cluster, but it's helpful to have used kubectl to spin up and debug a pod or two at some point.
However, I will say that there seem to be more Python-based jobs than C++ or Fortran jobs in the industry-based scientific computing world these days. Perhaps less so for fluid dynamics or finite element methods or other "simulation on a grid" problems, but there are a lot of python codebases out there in industry right now. I think it's very important to be comfortable working in higher level languages as well, and python is kinda becoming the lingua franca of a lot of sub-fields. For better or worse (I'm in the "better" camp), a lot of users are going to want a python library for what you're writing. It's best if you're used to "thinking in python" and can design a pythonic api.
I guess my fear was that all Python jobs (that I'll find) are going to be "machine learning" -- but really that would just mean data munging to feed Tensor Flow (or similar lib) and post process. Total snooze fest unless "post process" means something more like design generation/variation/optimization - and we are back to the api.