24 mathematics Postdoctoral positions at Oak Ridge National Laboratory in United States
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE1 [#27205] Position Title: Position Type: Postdoctoral Position Location: Oak Ridge
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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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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE [#27204] Position Title: Position Type: Postdoctoral Position Location: Oak Ridge
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE2 [#27206] Position Title: Position Location: Oak Ridge, Tennessee 37831
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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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physics, materials science, applied mathematics, computer science, or a related field, and no more than five years of experience beyond PhD. Preferred Qualifications: Background in quantum magnetism
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complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section of the Computer Science and Mathematics (CSM) Division. CSM delivers fundamental and applied research
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Requisition Id 15663 Overview: We are seeking a Post Doctoral Research Associate who will focus on high fidelity building energy modeling and advanced control. This position resides in the Integrated Building Deployment and Analysis Group in the BTSD, ESTD at Oak Ridge National Laboratory...
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE5 [#27233] Position Title: Position Location: Oak Ridge, Tennessee 37831
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mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design