48 computing-"https:"-"https:"-"La-Rochelle-Université" Postdoctoral positions at Oak Ridge National Laboratory
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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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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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in ORNL’s Center for Radiation Protection Knowledge (CRPK). The candidate will work with experts in computational radiation dosimetry and risk assessment. The candidate should be an independent thinker
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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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seeking postdoctoral candidates to investigate the mechanical and thermophysical behavior of irradiated metals and ceramics using advanced experimental and computational methods. The selected candidates
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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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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 15537 Overview: We are seeking a Postdoctoral Research associate in computational nuclear physics. This position focuses on nuclear theory with an emphasis on nuclear structure 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