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, and biological signals to construct coherent models of microbial organization across scales. Success in this role will require creativity in computational imaging, machine learning, and signal
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-be-completed PhD (typically completed within the last 0-5 years) in chemistry, chemical engineering, materials science or related field. Knowledge and hands-on experience in the following areas
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), in collaboration with Argonne researchers and external partners from industry and academia. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in
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field Experience leveraging artificial intelligence or machine learning in the development of battery electrolytes and catalyst materials Demonstrated expertise in lithium–sulfur battery materials and
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++, or JavaScript Experience with AI or machine learning techniques, including large language models or agentic systems Experience developing or integrating interactive visualization systems, including web-based
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processes Implement models and perform numerical simulations Analyze and interpret simulation and experimental data Integrate AI tools and workflows to enhance modeling and accelerate learning Collaborate
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help shape technologies with broad relevance to future quantum systems. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physics, electrical engineering
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well as in industry and at other national laboratories. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years)in Engineering or similar program. At least 2
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requires hands-on laboratory work to develop and synthesize and analyze new organic materials, specifically new battery electrolyte materials. Position Requirements Recent or soon-to-be-completed PhD
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. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemistry, materials science, metallurgy, electrochemistry, electrometallurgy, chemical