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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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, 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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expertise in lithium–sulfur battery materials and electrolyte systems Strong skills in the synthesis and structural optimization of host materials for lithium–sulfur batteries Experience in the
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, optimization, and a solid grasp of the interplay between synthesis methods and observable material characteristics. Ability to model Argonne's core values of impact, safety, respect, integrity and