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The Nuclear Science and Engineering (NSE) Division is seeking a postdoctoral appointee to develop computational methods and computer codes to model the physics and engineering of advanced nuclear
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the Scaling Machine Learning (SML) effort of the High-Energy Physics Center for Computational Excellence (HEP-CCE), the candidate will be responsible for facilitating the scaling of HEP ML workflows
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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analyze the results with the goal of quantifying the fundamental fluid mechanics of the systems and will work with computational modelers to develop a better understanding of the injection and breakup
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is supported by a DOE-funded research program on ultrafast science involving Argonne National Laboratory, University of Washington, and MIT. The goal of this research program is to understand and
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-inspired research relevant to microelectronics. The candidate will be part of a highly interdisciplinary project involving X-ray scientists, physicists, materials scientists, and computational scientists
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, computational physics and x-ray science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of the world’s largest supercomputers (Polaris
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to state-of-the-art research facilities and gain in-depth knowledge of the research frontiers of in situ characterization of deposition science and electrochemical interfaces. Position Requirements: A PhD in
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The Multiphysics Computation Section within the Transportation and Power Systems Division at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidate’s
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prototype, benchmark, and evaluate strategies to better support these workloads for Aurora. Position Requirements Required skills and qualifications: A recent PhD (within 5 years) in computer