64 cloud-computing-postdoc positions at Oak Ridge National Laboratory in postdoctoral
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high level of scientific productivity Publish scientific papers in key journals and present at key meetings Ensure compliance with environment, safety, health, and quality program requirements Maintain a
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postdoctoral fellows, scientific and engineering staff, visiting scholars, as well as a large network of international collaborators. Networking opportunities extend to the group’s broad research program in low
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environment, safety, health, and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics. Develop strong, productive working relationships across
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of using existing and emerging grid assets to ensure grid reliability and affordability of energy supplies. The group focuses on advanced grid modeling using advanced computing resources (e.g., quantum
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publish scientific results in peer-reviewed journals on time. Ensure compliance with environment, safety, health, and quality program requirements. Maintain strong dedication to the implementation and
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temperatures based on these predictions. The position resides in the Nanomaterials Theory Institute (NTI) within the Theory and Computation Section (TACS) at the Center for Nanophase Materials Sciences (CNMS
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them sent to [email protected] (For postdocs, use [email protected] ) with the position title and number referenced in the subject line. Instructions to upload documents to your
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data
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outcomes in peer-reviewed journals in a timely manner. Ensure compliance with environment, safety, health, and quality program requirements. Maintain strong dedication to the implementation and perpetuation
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computational foundations of that capability and help bridge the gap between Bayes theory and practical application: knowledge integration, developing robust likelihood frameworks, sampler behavior for long