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://arxiv.org/abs/2509.00098 ) This project sits at the intersection of artificial intelligence and materials characterization and modeling. The goal is to create an AI system that can intelligently operate
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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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addition, the postdoctoral appointee will perform advanced characterization of battery materials using synchrotron-based X-ray techniques to develop mechanistic understanding of material behavior and
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and lanthanides within controlled atmosphere gloveboxes. Apply chemical thermodynamic and kinetic theories to understand processes and develop models of material interactions and behavior in molten salt
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developing advanced instrumentation for CMB measurements at remote sites, including the South Pole. In addition to CMB instrumentation, the position offers opportunities to investigate novel materials and
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analysis frameworks that enable rapid interpretation of scattering measurements and facilitate the training of intelligent agents capable of guiding experiments and simulations in catalytic materials
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among material properties, electrochemical performance, and battery system cost at the material, cell, and pack levels. The researcher will plan and advance performance and cost modeling of energy storage
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and lanthanides within controlled atmosphere gloveboxes. Apply chemical thermodynamic and kinetic theories to understand processes and develop models of material interactions and behavior in molten salt
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models for microelectronics materials Curate, manage, and integrate heterogeneous datasets from experiments and simulations Collaborate closely with experimental teams to benchmark and refine computational
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Argonne National Laboratory is seeking a highly skilled Postdoctoral Researcher to work in the Separations and Bioprocessing Group in the Applied Materials Division (AMD) on developing, designing