64 computer-programmer-"https:"-"Inserm" "https:" "https:" "https:" "https:" "J" "J" Postdoctoral positions at Oak Ridge National Laboratory
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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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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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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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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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challenges facing the nation. The Computational Coupled Physics (CCP) Group within the Computational Sciences and Engineering Division (CSED), at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral
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resources. Present and report research results and publish scientific results in peer-reviewed journals in a timely manner. Ensure compliance program requirements for environmental, safety, health, and
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Requisition Id 15603 Overview: The National Center for Computational Sciences (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral research associate in the area of HPC
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Requisition Id 15217 Overview: Oak Ridge National Laboratory (ORNL), the U.S. Department of Energy’s largest multi-program science and energy laboratory, has an extraordinary 80-year history
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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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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