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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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Under the guidance of a supervisor, the Postdoctoral Researcher will conduct research in electrochemical energy storage to support the Battery Performance and Cost (BatPaC) modeling team
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High Energy Physics Division, the successful candidate will contribute to the design, fabrication, and characterization of superconducting devices based on microstrip-coupled TES technology, with
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for application to molten salt reactors (MSRs) and molten salt fuels, pyrochemical treatment of used nuclear fuel, and innovative applications in advanced energy systems being developed to meet the national energy
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++ preferred). Knowledge of power system operations, electricity market mechanisms, and energy system modeling. SAnalytical and problem-solving skills, with the ability to work independently and collaboratively
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-driven modeling (including ML/AI where appropriate) to help anticipate vulnerabilities and inform decision-making for energy deployment and national competitiveness. In this role you will : Conduct and
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collaborators across institutions to enable integration of scattering-derived insights into physics-informed AI models. Document research results, contribute to publications in peer-reviewed journals, and present
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qualified candidates to conduct theoretical research on dark matter and physics beyond the Standard Model, with a particular emphasis on opportunities enabled by novel quantum magnonics technologies
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, supply chain risk analysis, and data-driven modeling (including AI/ML where appropriate) to help inform decision-making for energy deployment and national competitiveness. In this role you will : Conduct
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emitters, including photon correlation and spectroscopy measurements About the Team You will join a collaborative team of physicists and engineers drawn from Argonne's High Energy Physics (HEP), X-ray