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or tools relevant to scientific visualization, ML/AI, HPC, and statistics. Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across
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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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: 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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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Basic Qualifications: Ph.D. in mathematics, engineering, data/computational science
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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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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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mathematics and scientific computing. This prestigious postdoctoral fellowship is supported by the Applied Mathematics Research Program in the U.S. Department of Energy’s Office of Advanced Scientific Computing