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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
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, and contributing to a robust data infrastructure that makes large-scale, multimodal datasets FAIR and AI-ready. You will publish findings in high-impact journals, present at major international
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible
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information Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork Desired skills, knowledge and abilities: Experience with large-scale molecular dynamics (MD) simulations
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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Because of the drastically increasing demand from AI/ML applications, the computing hardware industry has gravitated towards data formats narrower than the IEEE double format that most computational
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, interdisciplinary environment with access to large-scale computing resources and diverse scientific use cases. The position strongly supports publishing in top-tier venues, contributing to open-source research
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: Expertise in physics-based modeling, ideally electrochemical modeling Effective oral and written communication skills Experience with analyzing large and/or complex data sets Job Family Postdoctoral Job
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for critical energy and technology sectors. Ability to assess the economic and operational impacts of large-scale AI adoption (e.g., data centers, compute infrastructure) on U.S. electricity demand, generation