85 assistant-professor-computer-"https:" "https:" "https:" "https:" Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 16757 Overview: The Computational Hydrology and Atmospheric Science (CHAS) Group at Oak Ridge National Laboratory (ORNL) is seeking a highly motivated Postdoctoral Research Associate
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-performance computing, scientific modeling, and domain-informed AI to accelerate discovery across DOE mission areas such as energy, materials, biology, nuclear science, autonomous laboratories, and scientific
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scientific imaging. The primary responsibility of this position is designing and implementing sparse algorithms for large-scale scientific and numerical computations. The successful candidate will pursue
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solutions to compelling problems in energy and security. We are seeking a Postdoctoral Research Associate who will support the Science Engagement Section in the National Center for Computational Sciences
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imaging of cells and tissues. This position resides in the Quantum Computing and Sensing Group in the Computational Science and Engineering Division (CSED), Computing and Computational Sciences Directorate
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high temperature materials using conventional and novel manufacturing techniques. Perform microstructural characterization of alloys and assist in correlating microstructure with processing and mechanical properties
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solutions to compelling problems in energy and security. We are seeking a Postdoctoral Research Associate who will aid the research staff in understanding the synergistic effects of various degradation
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. Publish scientific results in high-impact peer-reviewed journals in a timely manner. Ensure compliance with environment, safety, health and quality program requirements. Maintain strong dedication
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Requisition Id 16984 Overview: We are seeking a highly motivated Postdoctoral Research Associate to conduct research at the intersection of quantum computing and high-performance computing (HPC
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computational foundations of that capability and help bridge the gap between Bayes theory and practical application: knowledge integration, developing robust likelihood frameworks, sampler behavior for long