59 computer-security "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" research jobs at Oak Ridge National Laboratory in United States
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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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liquids, frustrated magnetism, excitonic magnets, and strongly correlated electron systems. You will work closely with theorists, experimentalists, and computer scientists to build robust, scalable
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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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environmental conditions, and predicting photosynthesis at multiple scales. The selected postdoctoral scientist will work with a team of mathematicians, computational scientists, plant geneticists and
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methods towards improving our understanding of unique target materials. You will be working with scientists, engineers, technicians, and safety and quality assurance staff to support material testing and
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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