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available codes and existing high performance computing (HPC) infrastructure Identify key physics of the systems through simulations to drive actionable design recommendations Identify gaps between existing
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Scientific Machine Learning. The successful candidate will develop and deploy state-of-the-art SciML algorithms in high-performance computational physics codes. We accept applications from all candidates with
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astrophysics. The successful candidate will lead the implementation of new features in the AthenaK code, in collaboration with Dr. David Radice (Penn State) and Dr. Jim Stone (IAS), and they will participate
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