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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral research position focused on developing AI/ML methods for autonomous materials
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and tools to support engineering analyses of power grid planning and operations. Some of these tools will require the development of underlying optimization methodologies and analytics frameworks
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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
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research in radiochemical separations and advanced aqueous recycling of used nuclear fuel (UNF). The successful candidate will contribute to the development and optimization of advanced aqueous recycling
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optimization strategies to accelerate large-scale design studies and parameter scans. Contributing to the development of a conceptual design report for a scalable compact XFEL facility, including
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backscatter diffraction, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral
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catalytic systems Excellent written and oral communication skills Experience with time-dependent density functional theory (TDDFT) and excited-state optimization, which is considered an asset Ability to
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, including machine learning and Bayesian optimization approaches. Specific experience modeling the performance of advanced engineering ceramics and/or the high temperature performance of metal alloys
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optimization, as well as material characterization will be pursued through various strategies. The candidate must possess a strong foundational understanding of fundamental electrochemistry and have hands-on
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, Physics, or a related field. Strong expertise in machine learning, computational imaging, computer vision, or signal processing. Proficiency in scientific programming and modern ML frameworks, with