51 parallel-and-distributed-computing-"UNIS" Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 15602 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
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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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supercomputers, and computational imaging. Research Areas of Interest (include but are not limited to): Vision Transformers and foundation models for scientific and biomedical imaging Federated and distributed
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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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Theoretical Physics or a related discipline completed within the last 5 years. Experience with High Performance Computing and programming for massively parallel computers. Experience with quantum many-body
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/O solutions (e.g., HDF5, ADIOS2), and distributed computing tools relevant to data preparation. Evidence of ability to conduct independent research and publish in peer-reviewed venues. Preferred
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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-POSTDOCTORALRESEARCHASSOCIATE [#27204] 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