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design spaces. In this role, you will lead a research program centered on AI-driven autonomous synthesis, including: Active learning and Bayesian optimization over synthesis parameters such as
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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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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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, morphology, and device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and
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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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Mission projects: 1) Lattice QCD at the Intelligence Frontier (Phase II), and 2) AI-Enabled Optimization of Quantum Circuit Design for Realistic Nuclear Problems (Phase I); and two Topical
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operational optimization. Familiarity with distributed control architectures, industrial networking, motion control, and high-availability environments. Experience supervising or mentoring scientific