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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
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computational models, systems, and AI/ML tools using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. In
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/C++, or similar languages. Ability to develop, validate, and document research prototypes, algorithms, and technical workflows. Strong written and verbal communication skills for interdisciplinary
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discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC environments Experience with parallel computing environments, HPC in a Linux
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discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC environments Experience with parallel computing environments, HPC in a Linux
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discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC environments Experience with parallel computing environments, HPC in a Linux
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algorithms at scale on ORNL's computational resources, including the Frontier supercomputer, addressing critical challenges in science and engineering. Communicate and coordinate experimental results with
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Computing Methods for Physical Sciences Section in CSED. The MsM group is focused on delivering multiscale, multi-fidelity computational models and systems using algorithms and analytics for materials and
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electromagnetic transient (EMT) simulations, high-fidelity dynamic and transient power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and
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) with a particular focus on event forecasting, predictive maintenance, and corrective actions. The role would encompass working on forecasting, predictive, and text generation algorithms with Large