Arman Duha

Background

About Arman Duha

I’m a computational physicist and Ph.D. candidate with a strong foundation in numerical modeling, data analysis, and high-performance computing. My work centers on simulating complex physical systems ranging from quantum materials to energy devices and extracting actionable insights from large-scale scientific data. I bring hands-on experience with time-evolution algorithms, Monte Carlo methods, differential solvers, and machine learning techniques such as symbolic regression and feature engineering. I’ve developed and optimized simulation pipelines in Python, C++, and Bash for HPC environments, applying these to problems like wave transport in disordered media, spin dynamics, solar cell efficiency modeling, and defect-driven material behavior. My projects span academic and applied R&D domains, including DOE- and NASA-funded efforts, and have led to 6 publications in Physical Review A, Physical Review B, Optical Materials, and others. I’m especially interested in applying these skills in data-driven R&D environments to solve complex problems in materials discovery, energy technology, geophysical modeling, or quantum applications.