58 computational-physics-superconductor Postdoctoral positions at Oak Ridge National Laboratory
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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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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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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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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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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
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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Requisition Id 16217 Overview: The Multiscale Biomedical Systems Group within the Advanced Computing in Health (ACH) section of the Computational Sciences and Engineering Division (CSED) at Oak
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), Energy Science and Technology Directorate (ESTD), at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop physics-based computational models, including Finite Element Analysis (FEA
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array of capabilities in nuclear nonproliferation, data analytics, cybersecurity, cyber-physical resiliency, geospatial science, and high-performance computing, our organization seeks to produce world
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‑correction, calibration, and adaptive data‑acquisition methods to improve measurement efficiency and throughput Apply physics‑based or computational transport modeling to interpret internal material gradients