48 high-performance-computing Postdoctoral positions at Oak Ridge National Laboratory
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at the intersection of applied mathematics, computational science, and high-performance computing. The successful candidate will help develop advanced scientific computing methods and deploy them on state-of-the-art
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of two years of recent experience in nuclear reactor analysis and/or reactor analysis methods development Experience with various operating systems, high-performance computing, and SLURM systems
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Requisition Id 16984 Overview: We are seeking a highly motivated Postdoctoral Research Associate to conduct research at the intersection of quantum computing and high-performance computing (HPC
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: Conduct research and develop next-generation CFD methods (with finite volume and finite element techniques) and models for a variety of nuclear energy systems, using world-leading high-performance computing
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of irradiated ceramics and alloys for tritium technology development using advanced experimental and computational methods. The researcher will perform characterization of model systems using techniques such as
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Science and Technology Directorate at Oak Ridge National Laboratory (ORNL). Our research team carries out high-quality R&D focused on the development of new, high performance, lower-cost, domestically
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professionals to accelerate scientific discovery and engineering advances across a broad range of disciplines. As an important part of the broader High-Performance Computing (HPC) infrastructure, the division
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methods Computational or process modeling using tools such as Python, MATLAB, Aspen, COMSOL, Fluent, or OpenFOAM Experience with high performance computing environments Experience working in a classified
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for several key U.S. industries. As part of this role, the ideal candidate will be passionate about manifesting higher-level project deliverables into a timely, high-quality product. The immediate project needs
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Requisition Id 17070 Overview: We are seeking a Postdoctoral Research Associate who will use multiscale modeling and simulation to develop probabilistic lifting frameworks for high