33 high-performance-computing Postdoctoral positions at Oak Ridge National Laboratory
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, high performance computing and deep learning. The candidate will work in a collaborative research and development environment focusing on designing, implementing, and applying robust and high performance
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Directorate (PSD) at Oak Ridge National Laboratory (ORNL). This position offers an exciting opportunity to contribute to research in nuclear theory using high-performance computing and advanced many-body
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). Knowledge of high-performance computing or cloud environments for large-scale data. Strong collaboration skills and ability to work in interdisciplinary teams. Special Requirements: Applicants cannot have
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the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC
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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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of high-performance computing and its applications. An excellent record of productive and creative research, as demonstrated by publications in top peer-reviewed journals. Strong problem-solving skills and
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making
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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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computing AI on High-Performance Computing (HPC) cluster. Examples on areas of research interest include but are not limited to: Vision transformers. AI foundation models. Computing and energy-efficient
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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