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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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these fforts. A successful candidate will collaborate with scientists in DIS, Environmental Sciences division and Argonne leadership Computing Facility to further develop the AI foundation models
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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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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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accelerator physics, beam dynamics, or free-electron-laser science. Experience with machine learning, surrogate modeling, optimization, or AI-enabled scientific computing. Experience working with complex
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complex algorithms. Experience using computing clusters for simulation and data analysis. Experience with accelerator modeling and simulation codes, such as Elegant and Cheetah. Experience with SDDS, Tcl/Tk
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closing this gap. 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
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winds, land–atmosphere interactions, or aerosol–cloud interactions Strong experience in numerical modeling and high-performance computing • Experience applying AI/ML methods to model development, with
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with computational scientists in other divisions, including Mathematics and Computer Science (MCS) and the Argonne Leadership Computing Facility (ALCF). The group has significant involvement in three