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developing foundational statistical/AI/data-driven techniques for uncertainty quantification and visualization of complex, large-scale 2D/3D scientific data. Publishing research in leading peer-reviewed
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of the trained models to determine underlying processes that govern the given data. This job offers an excellent opportunity to conduct exceptional and innovative research in mathematics, statistics and scientific
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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 the laboratory. Ability
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, statistical, probabilistic, or algorithmic solutions to real world problems in the healthcare and biomedical research. As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has
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field completed within the last five years. Good track record in scattering theory, quantum many-body theory, thermodynamics, statistical mechanics, or non-equilibrium physics. Experience in
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structure and statistical mechanics codes, and data science tools would be highly desirable Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and
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and analyze system performance statistical data to improve the quality of the network environment. Adhere to a customer serviced focused culture. Deliver ORNL’s mission by aligning behaviors, priorities
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. Experience working with large environmental datasets such as flux tower and remote sensing data. Skills in statistically based model evaluation using observational data. Evidence of leadership potential
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will be part of a dynamic team within the National Security Sciences Directorate, working alongside experts in data science, statistics, nuclear engineering, and scientific computing to support high
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, evaluation, packaging, and deployment Conduct rigorous statistical analysis to aid in data exploration and interpretation Benchmark and T&E AI/ML systems against performance metrics and robustness; define and