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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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, including hybrid simulations coupling machine learning with numerical methods, multiscale discretization, nonlocal closure modeling, structure preservation, multilevel and multifidelity machine learning
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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, and deployment
Searches related to parallel computing numerical methods
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