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-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency and reliability of scientific discovery
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, work together, and measure success. Basic Qualifications: A PhD degree in Computer Science or a related discipline. A strong background in scientific data visualization, uncertainty quantification, AI/ML
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complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section of the Computer Science and Mathematics (CSM) Division. CSM delivers fundamental and applied research
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topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work
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for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section
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dimensional reduction methods and visualization tools. Effective writing and communication skills as demonstrated in publication and presentation. Demonstrated ability to work both independently and
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. To support ORNL strategic research and development activities at the Manufacturing Demonstration Facility (MDF) (https://www.ornl.gov/facility/mdf/ ), we are accepting applications for Postdoctoral Research