43 computer-security-"https:"-"https:"-"https:"-"IFM"-"IFM" Postdoctoral positions at Oak Ridge National Laboratory
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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for computational methods, manufacturing, and materials science. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service
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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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solutions to compelling problems in energy and security. The Environmental Sciences Division of Oak Ridge National Laboratory (ORNL) seeks a creative individual for a postdoctoral research associate position
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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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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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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