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protein structure and protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models of disease and digital twin applications. We
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decisions. RL & Self-Improving Models: RLHF/RLAIF, online RL, self-play, open-ended discovery, reward modeling, curriculum/active learning, data selection, iterative post-training, safety alignment and
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decisions. RL & Self-Improving Models: RLHF/RLAIF, online RL, self-play, open-ended discovery, reward modeling, curriculum/active learning, data selection, iterative post-training, safety alignment
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using imaging and computer vision; AI models of disease and digital twin applications. Biological applications and disease areas ideally focus on genetics and genomics. We also welcome applicants with
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systems through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to
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in the National Center for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of
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in the National Center for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of
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Geospatial Data & Computing Center to support ORNL’s geospatial high-performance computing (HPC) research portfolio within the Geospatial Science and Human Security Division. ORNL is the nation’s
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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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, system, and data-center architecture by analyzing the latest data center workload trends, Deep Learning (DL) research, analyst reports, competitive landscape, and token economics Cluster Management and