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. Candidates should have demonstrated interest and expertise at the interface of high energy physics, dark matter phenomenology, condensed matter physics, and quantum information science. In addition to the core
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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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existing efforts in the group and the division. The Argonne High Energy Physics Division provides a vibrant and collaborative research environment. In addition to a strong theory program, the Division has
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physics (HEP) and nuclear physics (NP) experiments. The successful candidate will be a key member of a multidisciplinary co-design team integrating materials science, computing, and device engineering to
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, Safeguards and Security policies, work rules, and safe practices Position Requirements Ph.D. in Materials Science, Physics, Chemistry, Computer Science, or a related field Proven research track record in
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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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-directed, collaborative research aligned with CNM’s strategic plan and for user support, including enabling workflows that couple computation, AI, and experimental measurement. Expertise in one or more of
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, the ALCF is studying the application of these techniques to a variety of our science applications, including but not limited to: Computational Chemistry, Plasma Physics, High Energy Physics, analysis
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science, engineering, computational science, a physical science (materials science, chemistry, physics etc.), or related field. Hands-on experience with AI frameworks and employing large language models. Strong Python
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Knowledge of atmospheric dynamics, process scale models, and numerical computation techniques Knowledge of data analysis Knowledge of using atmospheric observational datasets, data assimilation techniques