52 parallel-and-distributed-computing positions at Oak Ridge National Laboratory in computer-science
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Requisition Id 17148 Overview: The Fusion Energy Division at the Oak Ridge National Laboratory seeks applications for a skilled Computational Magnetohydrodynamics Engineer, with intended focus
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experience in HPC architecture, cluster management, and parallel computing, with a proven ability to work within highly secure and regulated environments. This role involves close collaboration with security
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are looking for candidates with extensive experience in HPC architecture, cluster management, and parallel computing, with a proven ability to work within highly secure and regulated environments
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executing in a high-performance computing environment Use of AI/ML techniques to develop surrogate models and support scientific discovery Experience in multiphysics modeling Experience in parallel
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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing for lattice
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Requisition Id 17002 Overview: We are seeking two Computational Instrument Scientists who will focus on developing software analysis tools for the diffraction instruments. These positions reside in
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(Accelerating Competitiveness through Computational ExceLlence), an industrial partnerships program within the National Center for Computational Sciences (NCCS). This position can be on-site, remote, or hybrid
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Requisition Id 16946 Overview: We are seeking a Computational Instrument Scientist who will focus on developing software analysis tools for the powder diffraction instruments. This position
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marked by operational excellence and engagement with the communities where we live and work. ORNL invites you to apply to its Distinguished Staff Fellowship (DSF) Program. This program recognizes
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an emphasis on highly scalable algorithms for exascale HPC environments Experience with parallel computing environments, HPC in a Linux environment Experience with surrogate modeling Experience with data