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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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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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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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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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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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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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Quantum Theme, focusing on Next-Generation Quantum Systems. The successful candidate will lead efforts to discover and design quantum emitters with desirable properties for quantum information science (QIS