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learning (ML) to address future physics and detector challenges. Current physics interests include Standard Model measurements and searches for new phenomena. We welcome applicants who are excited
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the development of AI models for robotic control and the demonstration of these methods via simulation and experiment. Beyond the listed projects, the candidate may contribute to other large-team scientific
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that influence the development, intensification, and persistence of extreme events, using observational datasets, machine learning, and Earth system modeling. The successful candidate will work with observational
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modeling and techno-economic assessment as appropriate. The candidate will work independently under general guidance, collaborate effectively within a multidisciplinary team environment, and prepare
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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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(CSTR), with control over morphology, particle size, and composition Conduct physical, chemical, thermal, and electrochemical characterization of layered oxide cathode materials using a range of
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centralized. The successful candidates will work at the intersection of federated learning, foundation models, multimodal biomedical AI, privacy-preserving machine learning, continuous learning, and agentic AI
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components. Work closely with a beam diagnostics physicist and controls group engineer to install and optimize diagnostic systems. Develop and integrate mmWave diagnostics equipment for beam position
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laboratory workflows. The position will focus on building the data resources, predictive models, and closed-loop decision frameworks needed to accelerate experimentation and advance next-generation autonomous
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design, fabrication, and/or measurement Proficiency in low-noise cryogenic measurement techniques at millikelvin to few-kelvin temperatures Ability to model Argonne's core values of impact, safety, respect