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postdoctoral research associate to advance the state of scientific AI by addressing cross-cutting challenges in data readiness for AI to enable scalable, reproducible AI workflows on leadership-class systems
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Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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science, computer science, computer engineering, electrical engineering, and optical engineering, and frequently collaborates with partners in industry, academia, and other government organizations
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, Cloud, and HPC continuum. This position centers on advancing intelligent workflows that enable seamless processes in autonomous discovery, complex data integration, workflow provenance, and interactive
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Group in support of the Nuclear Structure and Nuclear Astrophysics research program and the Nuclear Data effort. Major Duties and Responsibilities: Design, propose, perform and analyze experiments in
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Requisition Id 15813 Overview: We are seeking a highly motivated postdoctoral researcher with a strong background in sensor integration, data acquisition, and in situ process monitoring
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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will involve designing beam dynamics experiments, measurement, simulation, and data analysis. This position resides in the Accelerator Physics Group in the Accelerator Science and Technology Section
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scattering data with complementary techniques at ORNL, to understand critical mineral formation and extraction. This work will be conducted with close interaction and collaboration with other scientists within