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Requisition Id 17231 Overview: We are seeking a Postdoctoral Research Associate who will focus on the development, application, and optimization of an active learning workflow for dataset
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an ambitious research agenda that advances trustworthy, interpretable, and scalable AI for science while collaborating with leading experts in machine learning, optimization, scientific computing, domain
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strong depth in one or more relevant areas and interest in collaborating across disciplines. As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year
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develop new methods to address scientific and engineering problems, collaborate with leaders in your field and across the laboratory, and disseminate innovative results through publications and
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cross-facility collaborative environment Present and report research results and publish scientific results in peer-reviewed journals in a timely manner. Ensure compliance with environment, safety, health
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. To conduct this work, the successful candidate will use the world's first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric grid analytics, and
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from the SPRUCE (Spruce and
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from the SPRUCE (Spruce and