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position will contribute to the Forge project, which is focused on developing advanced multimodal AI capabilities that can learn across distributed data environments without requiring sensitive data to
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computing, including distributed computing, high-performance computing (HPC), or machine learning (ML) Interest in Standard Model measurements and/or searches for new phenomena Application Review Review of
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Directed Research and Development (LDRD) project. This research focuses on understanding how critical elements are distributed at mineral-water interfaces, with the goal of revealing the fundamental
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-based models for large dynamical systems (e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with
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with University of Notre Dame to develop novel on-chip superconducting quantum detectors with magnetic targets for low-energy axion dark matter detection. Key Responsibilities: Develop superconducting
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collaborative research team and work closely with scientists at Argonne, the University of Chicago, and partner institutions. Candidates should have a strong background in experimental physics, engineering
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evaluation of process performance, with opportunities to collaborate with scientists, engineers, and external partners across national laboratory, university, and industrial settings. Key
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as such, the candidate will work closely with fellow group members, as well as with research teams from the broader Argonne community, the University of Chicago Pritzker School of Molecular