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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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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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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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, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in Hydrology, Water
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: https://www.ornl.gov/content/research-integrity Basic Qualifications: To be eligible you must have completed a PhD in chemistry, physics, engineering, or a related field with in the last 5 years
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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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respectful workplace – in how we treat one another, work together, and measure success. Required Qualifications: PhD in Nuclear engineering, computer science, applied mathematics, or a related field completed
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treat one another, work together, and measure success. Basic Qualifications: A PhD degree in material science, mechanical engineering, electrical engineering, robotics, or a related field completed within
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PhD in materials science and engineering, physics, chemistry, or electrical engineering or a related field. Preferred Qualifications: Experience in scanning transmission electron microscopy Background