This is highly relevant to my field of specialty.

I am a fourth-year Ph.D. student, and my specialty is "Numerical simulation of stochastic partial differential equations". I am for the most degree self-taught in programming (except for the first introductory course in programming, which was a mix of C and Matlab) and have dabbled in C, Python, Java, R, Mathematica, and Javascript to varying degrees.

I am by far the most well-versed in Matlab. We design our numerical schemes and experiments, and the advantage of implementation speed by Matlab is difficult to beat (since most often under-the-hood problems are already solved to a satisfying degree), with the latest manuscript experiments soon reaching 3'500 lines of code. Our purpose is to investigate convergence rates and scheme properties, meaning that we are less concerned about commercial or the most efficient implementations.

That being said, I have actively pursued other languages as well to push the limits of what we can do. I'm simply not given the time to put it into practice. I have trashed two graphics cards due to Matlab trying to allocate memory beyond what's allowed. Some experiments take weeks or even months, and that requires me to get creative with how to deal with problems that come with it (such as power outages or forced updates).