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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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on Accelerating Fusion Technology and as an ORNL UT-ORII staff fellow, successful applicants will be part of a team of Research Faculty and ORNL staff to collaborate in the area of Fusion Materials. In this role
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, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear algebra, machine
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sensor research and development for energy applications, with a focus on advanced manufacturing technologies. The MSA group consists of staff members with backgrounds in mechanical engineering, materials