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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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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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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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/ML techniques for real-time, real-world data Transport and dispersion modeling Fate modeling of materials in the atmosphere Applied statistics Data analytics Deliver ORNL’s mission by aligning
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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in evolutionary biology, plant biology, genomics, bioinformatics, mathematics, statistics, computer