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modeling tools to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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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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. The candidate will be expected to conceive of, plan, and implement the scientific research, and to report relevant results in publications and conference presentations. The selected individual will have access
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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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reduction approaches and systems will be developed and implemented that operate close to instruments at the edge, and that leverage high-performance computing environments when needed. This position will be
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multidisciplinary team, the Postdoctoral Appointee will work at the intersection of AI/ML, climate science, and high-performance computing. The candidate will develop LLMs specifically designed to understand, process
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device operation. The project involves large-scale simulations on exascale computing resources to probe switching behavior while accounting for effects of defects, competing metastable phases, doping
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(electrochemistry, materials synthesis, or characterization) or computational simulations perspective, is required. Proficiency in Python programming is required. Familiarity with REST APIs is desirable. Master’s
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The X-ray Imaging Group (IMG) of the Advanced Photon Source (APS) is seeking a postdoctoral researcher with expertise in computational science and image processing to develop innovative methods