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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
-state physics, ferroelectrics and/or 2D materials. Strong background in developing and/or applying materials simulation methods, such as atomistic simulations using electronic-structure and/or machine
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of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work
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and nuclear structure and reactions. The position is part of the nuclear theory team that resides in the Theoretical and Computational Physics group in the Physics Division, Physical Sciences