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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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tools in combustion. Our computational codes are also used by various international research institutions. Both experimental and numerical projects are conducted in parallel providing a platform for
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; David Plant ECSE 335: Microelectronics; Gordon Roberts ECSE 343: Numerical Methods in Eng; Roni Khazaka ECSE 353: Electromagnetic Fields & Waves; Thomas Szkopek ECSE 354: Electromagnetic Wave Propagation
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lattice field theory and numerical methods, with experience in HPC programming (e.g., C++, Python, MPI, OpenMP, CUDA) and parallel computing environments. - Experience in performance analysis, debugging
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capability Fluency in relevant models, techniques or methods and ability to contribute to developing new ones High level of competence in computer programming, with C++ an advantage. Ability to communicate