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on practical AI systems that connect research prototypes to real-world deployment environments, including cloud, secure enclaves, trusted research environments, and leadership computing platforms
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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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. This is an exciting opportunity to work within a collaborative group with deep expertise in silicon detectors, Trigger/DAQ (TDAQ) systems, software, and computing. The Argonne ATLAS group plays a
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guide synthesis in real time AI-enabled analysis of high-throughput, multimodal experimental data with uncertainty quantification Integration of edge computing, high-performance computing (HPC), and
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is extended yearly. Position Requirements A recent PhD (within 5 years) in computational chemistry, chemistry, materials science, physics, computational science, computer science, engineering, or a
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usefulness for accelerator operation. Position Requirements PhD completed in the past 5 years or soon-to-be completed in accelerator physics or a related field is strongly preferred. PhD graduates in other
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. Position Requirements PhD completed within the last 5 years, or expected soon, in accelerator physics or a closely related field. Candidates with PhDs in physics, applied mathematics, computational science
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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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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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) as evidenced by their publications High performance computing (HPC) experience in code development with parallel programming techniques using the message passing interface (MPI) library Proficiency