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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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a unique opportunity to develop cutting-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency
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Sciences Directorate, at Oak Ridge National Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) and machine learning/artificial
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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toward integration of hydropower with battery storage and other technologies. Computational and analytical skills : Demonstrated ability in selecting and deploying machine learning tools (Random Forest
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laboratory investigations with inorganic mercury and methylmercury. Acquire and analyze data using a range of analytical instrumentation. Maintain detailed and accurate records. Prepare oral and written
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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-based characterization of physical properties (magnetic, transport, crystallographic, thermal, optical). Preferred Qualifications: Experience with bulk single crystal growth of inorganic materials
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and simulation tools, such as spreadsheet-based process cost modeling, input/output modeling, or commercially available life cycle analysis tools such as SimaPro and openLCA. Excellent written and oral