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: Experience in one more of the following areas: Mathematical tools for data analysis Numerical methods for differential and integral equations Modern machine learning software tools and frameworks
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance
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, exterior of buildings, laboratories, pipes, handrails and stairwells, floors, steel, and other varioussurfaces. Use numerous application methods such as brushing, rolling, and spraying. Apply epoxies, floor
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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within the last 5 years Preferred Qualifications: Experience in one more of the following areas: Mathematical tools for data analysis Numerical methods for differential and integral equations Modern
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Materials (MsM) group of the Advanced Computing Methods for Physical Sciences Section in CSED. The MsM group is focused on delivering multiscale, multi-fidelity computational models and systems using
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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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modeling group at the forefront of deploying novel computational engineering techniques for problems that are critical to U.S. national and energy security. We utilize our expertise in numerical
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modeling group at the forefront of deploying novel computational engineering techniques for problems that are critical to U.S. national and energy security. We utilize our expertise in numerical
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empirically informed methods to advance the fundamental engineering understanding of gas centrifuge systems. The group leverages analytical techniques and advanced computational tools—including finite element