Theoretically Possible
Kyle Bomeisl
My degree is in Mathematical Physics and I worked as a researcher for the Pixley Theoretical Condensed Matter Physics Group and as a software engineer for Ameriprise Financial. My professional experience includes code development and running high-fidelity simulations with parallel processing and high-performance computers (HPC), distributed computing, and Linux-based HPC clusters. I have a deep working knowledge of numerical linear algebra, numerical solutions to ordinary, stochastic, and partial differential equations, large sparse matrix problems, finite difference, finite element, Monte Carlo simulations, fast numerical methods, Galerkin methods, vector finite elements, integral equations, numerical algebra, Multi-level Fast Multipole Methods, engineering optimization, and cold atom dynamics. I'm proficient in Python (including libraries like NumPy, SciPy, and Matplotlib), Java, Kotlin, Julia, and C++, as well as cleaning data and presenting results in useful formats using tools such as Excel, MATLAB or Mathematica. I have in-depth knowledge of job scheduling tools like SLURM with an ability to configure and optimize job submissions for computational research, performing Condensed Matter Theory and Materials Science simulations on local computing clusters and HPC systems, and writing high-quality software with adherence to professional best practices including using Git, unit testing, and documentation
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