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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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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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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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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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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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learning (ML) to address future physics and detector challenges. Current physics interests include Standard Model measurements and searches for new phenomena. We welcome applicants who are excited
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an excellent team player. A high commitment to safety. Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. This position requires an on-site presence at the Argonne campus
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organized notebooks and project files. Proven ability to be an excellent team player. A high commitment to safety. Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork
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. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred skills: Microwave circuit design, terahertz optics, and their characterization. Knowledge about near
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that influence the development, intensification, and persistence of extreme events, using observational datasets, machine learning, and Earth system modeling. The successful candidate will work with observational