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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance
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energy goals. ESIA also develops, deploy, and advance grid technologies that ensure a robust and secure U.S. grid transmission and distribution system. We collaborate with government agencies as
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Up-to-date awareness about the status and trend of the computing hardware industry Interest in working with domain experts on practical problems Experience with large-scale distributed systems
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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 Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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, distributed systems, or large-scale data pipelines Experience with game engines, XR systems, or advanced graphics frameworks applied to scientific data Record of publications in visualization, AI
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knowledge graph integration. Background in systems biology, genomics, protein engineering, or drug discovery. Familiarity with high-performance computing environments and scalable distributed systems
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the U.S. achieve energy goals. ESIA develops, deploy, and advances grid technologies that ensures a robust and secure U.S. grid transmission and distribution system. ESIA also collaborates with government
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total X-ray scattering (TXS) and pair distribution function (PDF) analysis capabilities and methodology to study laser-driven structural dynamics in functional materials. This position is part of a
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling