75 assistant-professor-computer Postdoctoral positions at Oak Ridge National Laboratory
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at the intersection of applied mathematics, computational science, and high-performance computing. The successful candidate will help develop advanced scientific computing methods and deploy them on state-of-the-art
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key findings in peer-reviewed journals in a timely manner. Collaborate within a multi-disciplinary research environment consisting of computational scientists, experimentalists, and engineers conducting
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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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Requisition Id 16765 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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key findings in peer-reviewed journals in a timely manner. Collaborate within a multi-disciplinary research environment consisting of computational scientists, experimentalists, and engineers conducting
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Requisition Id 16262 Overview: We are seeking a postdoctoral researcher to work at the intersection of tensor networks, quantum algorithms, scientific computing, topological physics, and quantum
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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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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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Engineer will conduct R&D in nuclear nonproliferation with expertise in computational nuclear reactor physics through modeling and simulation (M&S). The candidate will perform analysis and methods
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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