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on using neutron scattering to probe the structure and dynamics of materials relevant to energy applications and/or structural materials. In particular, the work will aim to (1) understand how hybridized
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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environment. They are supported with mentorship, conference travel, and opportunities to develop independent research directions and build professional networks. Oak Ridge and the greater Knoxville area offer
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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MSTD on the physical metallurgy of aluminum alloys for various applications including automotive, aerospace, and thermal management. Projects will include application of physical metallurgy principles
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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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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