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limited to, ATLAS at CERN, the South Pole Telescope, and the Simons Observatory. The candidate is also expected to work closely with computational experts at the Computational Science (CPS) division
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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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lead a world-class research program that aligns with DOE priorities in low-energy nuclear physics outlined in the 2023 Nuclear Science Advisory Committee Long Range Plan for Nuclear Science. Additional
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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Intelligence, Machine Learning, Quantum Information and Quantum Simulation. The successful candidate will be expected to lead an independent research program in particle theory to strengthen and complement
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years ) Ph.D. in Engineering, Operations, Computer Science, Mathematics or a related field. Knowledge of optimization, power systems operations and planning, electricity markets, issues surrounding
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at technical conferences. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, computer science
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow
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PhD (within the last 0-5 years) in field of physics, chemistry, materials science, electrical engineering, or a related field Demonstrated expertise in electronic structure theory Experience with large