As others have stated in the comments, scientific computing in pure Python is likely to be too slow and possibly difficult to deploy on HPC nodes. Most use of

As others have stated in the comments, scientific computing in pure Python is likely to be too slow and possibly difficult to deploy on HPC nodes. Most use of Python in scientific computing and research tends to be as a high-level "glue" or scripting language that calls extremely optimized C, C++, or FORTRAN libraries. Most of the libraries involved are decades old, very mature, and widely used. Depending on what you are trying to do, it might be possible to use CUDA or other GPU-based parallelization libraries. Rust-based libraries might exist, but are likely to be considerably younger, possibly buggy for some problem-set edge cases, and less widely used than their corresponding C, C++, or FORTRAN counterparts. Ambition and building new things to solve problems is always good, but your professors know what works. If you are building new things, I would make sure to compare your work with existing solutions to verify that the new stuff is behaving correctly. Best of luck in your endeavors!