341 computer-programmer-"https:" "https:" "https:" "U.S" positions at Oak Ridge National Laboratory in United States
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Learning skills. This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML models are heavily
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Requisition Id 15757 Overview: We are seeking a Computational Scientist for the Multiscale Materials group in the Computational Science and Engineering Division (CSED). The candidate will be
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE4 [#27230] Position Title: Position Location: Oak Ridge, Tennessee 37831
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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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Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a preplacement drug test and participation in an ongoing random drug testing program. Visa Sponsorship: Visa
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response to upset conditions in the data centers. Operators are trained to the Computational Science Building Computer Center Operations Emergency Response Plan and Emergency Response Checklists. Upset
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offered a salary at or near the top of the range for a position. Link to benefits. https://jobs.ornl.gov/content/Benefits/?locale=en_US Overview: We are seeking an SAP Functional Analyst to serve as a
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testing program. In addition, due the SCI, you may also be subject to random polygraph testing. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has
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of new and ongoing data programs to position the group for future success. Work closely with data program PIs for identifying and addressing data lifecycle, metadata workflow and other data management
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and