50 postdoc-structural-engineering Postdoctoral positions at Oak Ridge National Laboratory
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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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
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. Research will involve growth of single crystals and measurements to understand their structural and physical properties including magnetism and thermal transport, as well as helping to identify new magnetic
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Requisition Id 15489 Overview: The Analytics and AI at Scale (AAIMS) group under Advanced Technology Section (ATS) of NCCS is hiring two postdoctoral research associates to push the frontier
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that will strengthen the nation’s leadership in creating solutions to help sustain the Earth’s natural resources. Our scientists conduct research, develop technology, and perform analyses to understand and
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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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, you will collaborate with a dynamic team of scientists and engineers, leveraging cutting-edge resources; most notably the Frontier supercomputer, the world's first exascale computing system. This is a
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
of NTI and CNMS to develop HPC workflows that can perform multi-fidelity simulations to predict and interpret a wide range of structural and electronic characterization techniques Develop physics-informed
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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