13 parallel-processing "the" Postdoctoral research jobs at Oak Ridge National Laboratory
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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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, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together
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. Collaborate within a multi-disciplinary research environment consisting of computational scientists, computer scientists, experimentalists, engineers, and physicists conducting basic and applied AI/DL research
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to cutting-edge research aimed at transforming scientific data management and workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent
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workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed
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developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation of results. Deliver ORNL’s mission by aligning
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techniques for the generation and exploration of complex, large-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable