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Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods to advance multi-scale coupled physics simulations in support of the missions and programs of the US
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industrial imaging data. You will directly contribute to developing and deploying algorithms for multi-modal tomography (X-ray, neutron, and electron), advancing methods for non-destructive evaluation (NDE
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phenomena of nuclear reactor fuel cladding and core internal materials. Strong materials characterization skills are required. This position resides in the Fuel Cladding and Core Internals (FCCI) Group in
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at appropriate national and international meetings. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. As a member
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RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent written and oral communication skills. Motivated self-starter with
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electronic structure theory (e.g., density functional theory), and machine learning based computational studies of molecular and periodic systems. The postdoc will also work within a multidisciplinary multi
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, diverse, and multi-institutional team, including experimentalists, design engineers, and computational scientists, on the design and optimization of tritium breeding blankets and fusion reactor cooling
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outcomes. The candidate will be a postdoctoral researcher within the Multiscale Materials (MsM) group for Physical Sciences Section in CSED. The MsM group is focused on delivering multiscale, multi-fidelity
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to compelling problems in energy and security. The Biosciences Division of ORNL has an opening for a postdoctoral researcher to integrate diverse research across the Center for Bioenergy Innovation’s (CBI, https
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Requisition Id 15287 Overview: The Manufacturing Systems Analytics (MSA) group at the Oak Ridge National Laboratory (ORNL) conducts applied machine learning, decision science, and multi-modal