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and numerical algorithms for modeling and simulation of nuclear systems. Computational Nuclear Engineers within the RTHPCM group will work with group, section, and division members and external
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a multidisciplinary team of hydrologists, Earth system scientists, and computational
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efficiency of modern and future high-performance computer systems under various workload characteristics through measurement, modeling, and simulation. Publishing papers in high-quality refereed conferences
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simulation and flood inundation modeling. River basin planning and operations modeling, including reservoir simulation and optimization. Hydrodynamic modeling of water temperature and quality constituents
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analysis (FEA)—to evaluate and predict the behavior of complex rotating machinery operating under extreme conditions. Through the integration of modeling, simulation, and experimental validation, the group
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, Integrity, Teamwork, Safety, and Service. Basic Qualifications: A PhD in Earth science, geography, environmental modeling, environmental science, or a closely related field, and at least two years of post-PhD
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power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and different applications of EMT simulations. Selection will be based
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simulations for fermionic and Hubbard-like materials models • Collaborate within a multi-disciplinary research environment consisting of quantum computing experts, computational scientists, and condensed
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expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material