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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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operational optimization. Familiarity with distributed control architectures, industrial networking, motion control, and high-availability environments. Experience supervising or mentoring scientific
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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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Prototyping Lab: https://rpl.cels.anl.gov/ Autonomous Discovery at Argonne: https://www.anl.gov/autonomous-discovery MADSci on GitHub: https://github.com/AD-SDL/MADSci AD-SDL organization on GitHub
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unprecedented scale. For more information: Rapid Prototyping Lab: https://rpl.cels.anl.gov/ Autonomous Discovery at Argonne: https://www.anl.gov/autonomous-discovery MADSci on GitHub: https
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availability. Relevant Publications: 1. P. Chen et al., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1. 2
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below: https://www.anl.gov/cse/atomic-molecular-and-optical-physics Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in physics, chemistry, materials science, or
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solver, capable of running simulations on the Aurora supercomputer, using AMReX ( https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry