43 machine-learning-postdoc-"https:" Postdoctoral positions at Oak Ridge National Laboratory
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topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work
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credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting
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within a multi-disciplinary research environment consisting of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency and reliability of scientific discovery
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), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and
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residency requirement, you will be required to obtain a PIV credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and
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Requisition Id 15358 Overview: Oak Ridge National Laboratory (ORNL) is seeking an ambitious postdoctoral scientist with keen interest in artificial intelligence (AI) / machine learning (ML) and
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Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) and machine learning/artificial intelligence (ML/AI) techniques that incorporate
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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