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, design of experiments, image and data processing. Position Requirements Recent or soon-to-be-completed PhD with strong background in Physics or Materials Science (within the last 5 years) Physics
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machine learning models at a world-class high-performance computing facility The candidate will have access to state-of-the-art computing resources, including: NVIDIA DGX-2 Systems: Powerful platforms
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leaching, solvent extraction, ion exchange, electrodialysis, membrane separation, and crystallization or precipitation. Position Requirements Recent or soon-to-be-completed PhD (typically completed within
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in the cleanroom at the Center for Nanoscale Materials. The candidate will work in a collaborative environment including a network of leading researchers. Position Requirements Recent or soon-to-be
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such models, and working with a team of scientists interested in pushing the boundary of predictability. Position Requirements Recent or soon-to-be-completed PhD (completed within the last 0-5 years) in
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total
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in high-impact venues Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of High Energy Physics or Nuclear Physics, or a closely related discipline
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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The position is part of a new collaboration between Argonne National Laboratory, the University of Notre Dame, and UIUC, supported by the Quantum Information Science Enabled Discovery 2.0 (QuantISED
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positions is to work on AI/ML with applications to cosmological modeling and surveys. Another open position is to work with Matthew R. Becker on weak gravitational lensing analysis with Rubin LSST data