325 computational-physics "https:" "https:" "https:" "UCL" positions at Oak Ridge National Laboratory
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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on facility operations schedules. Major Duties/Responsibilities: Development, execution, and analysis of experimental studies with emphasis on advancing pellet fueling physics through a combination of
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seeking postdoctoral candidates to investigate the mechanical and thermophysical behavior of irradiated metals and ceramics using advanced experimental and computational methods. The selected candidates
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characterization, topology optimization, and real-time sensing. CSED focuses on transdisciplinary computational science and analytics at scale to enable scientific discovery across the physical sciences, engineered
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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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Requisition Id 15603 Overview: The National Center for Computational Sciences (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral research associate in the area of HPC
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Requisition Id 15695 Overview: We are seeking a mid-level Computing Control Room Operator who will focus on responding to upset conditions in the data centers. This position resides in the HPC
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strategies. This leadership role will serve as the Group Leader for the Remote Sensing and Environmental Informatics (RESI) Group. The group’s mission is to build world-class capabilities in scientific data
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/Responsibilities: Performs and analyzes routine to moderately complex engineering functions, applying mathematical, physical science, and engineering technologies. Develop complex electro-mechanical equipment and
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance