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successful candidate will conduct research focused on meso- and macroscale mathematical modeling of next-generation batteries, while leveraging advanced artificial intelligence (AI) tools to accelerate
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learning, statistical modeling, or advanced analytics applied to complex industrial, energy, logistics, manufacturing, or supply chain systems. Experience developing and applying optimization models, such as
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operando experiments under electrical, thermal, or mechanical bias to capture real-time defect dynamics. Integrate multimodal datasets and collaborate with AI/ML teams for data fusion, physics-informed model
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of LLMs to accelerators, specifically, use of agentic AI to support intelligent system analysis, aid operator decision-making, automate complex workflows, and enable more adaptive approaches to machine
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relevant for data manipulation and analysis including experience with creating and using complex models in Simulink & scripting in Matlab, Python, R. A successful candidate must model Argonne’s Core Values
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, or related areas. Ability to design and conduct computational experiments, analyze model performance, and communicate results clearly. Experience working with large-scale or complex datasets, including
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in the synthesis and characterization of highly air-sensitive transition metal complexes Experience in the synthesis of polymeric materials Strong understanding of kinetics and thermodynamics as they
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complex mixtures. Day-to-day responsibilities will include designing and fabricating electrochemical reactor prototypes, conducting electrodeposition experiments in deep eutectic solvent (DES) media
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Argonne National Laboratory is seeking a postdoctoral researcher with expertise in life-cycle analysis, industrial process modeling, and critical minerals and materials assessment. The appointee
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Responsibilities : Spearhead the research and development of predictive models and strategic tools designed to support decision making in strengthening both domestic and international supply chains, particularly