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, technique development, and new initiatives to peer reviewers and Q-NEXT program managers. Position Requirements Completed Ph.D. within the last 0-5 years (or soon-to-be-completed) in condensed matter physics
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. Position Requirements A formal education in Physics, Materials Science, Chemistry, or a related field at the PhD level with zero to five years of employment experience. Demonstrated experience with high
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math, HPC, signal processing, computational physics and materials science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of
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(CFD) to develop and optimize new processes and equipment designs using high-performance computing Develop process- and facility-scale models as the foundation for digital twins of chemical processing
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, large-scale computational science, and simulation of networked physical systems Familiarity with techniques for sensitivity analysis and handling high-dimensional problems Experience in power grid
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. coastlines, including incorporation of levees, jetties, and wetlands. Ingest and process coastal datasets (bathymetry, topography, land use/cover) to support accurate wetting and drying and bottom friction
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interdisciplinary teams within the Materials Science division at the Argonne National Laboratory and external collaborators. Position Requirements • Ph.D. (completed or soon to be completed) in Physics, Materials
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, including synthesis of fuel materials and recycling of used MSR salts. The selected candidate will develop and optimize process chemistries to synthesize chloride and fluoride species, recover metals and
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. The successful candidate should have expertise and experience in process modeling, techno-economic analysis (TEA) and life cycle analysis (LCA) of lithium-ion batteries and/or recycling and resources to products