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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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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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ceramic engineering including hands-on, practical laboratory experience with material synthesis and processing, and analytical methods. Hands-on experience: XRD & Refinement, SEM-EDX, TGA-MS, DSC, BET, coin
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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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. The successful candidate will be a key contributor to a multidisciplinary co-design team spanning material science, computing, and electronic engineering, with the goal of enabling next-generation detector
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simulations and experiments across scientific user facilities, leveraging data to understand complex material phenomena across scales. Key Responsibilities Design, implement, and validate physics-informed AI/ML
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on securing a domestic supply chain for critical materials, such as graphite. This candidate will join a team developing and testing material beneficiation, leaching, and separation unit operations for carbon
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and written communication skills in scientific and engineering contexts. Ability to integrate diverse knowledge and perspectives to drive innovation. Experience working independently and collaboratively
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The Q-NEXT National Quantum Information Science and Research Center based at Argonne National Laboratory invites applications for a postdoctoral position to conduct research in the field of material
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morphology, product quality, and process efficiency at engineering scales. With guidance, the appointee will : Perform experiments with multimodal sensors to advance the technical understanding and application