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. The division leads and supports a vibrant world-class user program while continuing to develop new neutron scattering methods, approaches, and instrumentation, and performing cutting-edge scientific research
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require passing a pre-placement drug test and participation in an ongoing random drug testing program. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has
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attracting scientists from all over the world conducting world-class research in physical, chemical, and materials sciences. More details about the instruments and their scientific impact can be found at https
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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Requisition Id 16063 Overview: The Centrifuge Science (CS) Section within the Enrichment Science and Engineering Division (ESED) at Oak Ridge National Laboratory (ORNL) is seeking an Electric Motor
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Science or related discipline. Demonstrated experience in scientific data visualization, AI/ML, or a related field. Proficiency in the Python and C++ programming languages. Preferred Qualifications: Strong publication
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analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division, Computing and Computational
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scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation. ORNL offers competitive pay and benefits programs to attract and retain
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-quality parts, systems, and testing results supporting multiple programs. To organization managers, for world-leading science and technology directions, safe effective, and compliant operations, ownership
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visualization technologies, programming systems and environments, and system science and engineering. Major Duties/Responsibilities: The position requires collaboration within a multi-disciplinary research