22 research-assistant-learning Postdoctoral positions at Oak Ridge National Laboratory
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to scientific domains. Publish research outcomes in peer-reviewed journals and conference venues, setting benchmarks and proposing methodologies for cross-disciplinary readiness challenges. Aid in the development
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Research Associates to apply their hydrological and water resources expertise toward cutting-edge waterpower and engineering research in the Water Resources Sciences and Engineering Group at Oak Ridge
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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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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to differential privacy in federated learning, driving advancements in secure, collaborative AI systems globally. As a postdoctoral researcher, you will help solve some of the most challenging problems faced by
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Requisition Id 15823 Overview: We are seeking a postdoctoral researcher skilled in biogeochemistry who will contribute to mercury remediation technology development program, specifically focusing
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Requisition Id 15880 Overview: Oak Ridge National Laboratory is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative
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Requisition Id 15682 Overview: We are seeking a highly motivated Postdoctoral Research Associate to contribute to the NEUROPIX project, an interdisciplinary effort at the intersection of high-energy
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for additive manufacturing (AM). The candidate will aid staff in conducting high quality research by developing, integrating, and deploying advanced sensing modalities (e.g., thermal, optical, acoustic, etc
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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