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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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workflows, and immersive or experimental interfaces Integrate LLM-based and agentic AI systems with scientific visualization frameworks, in situ pipelines, and data analysis workflows Prototype and evaluate
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and machine learning techniques in analyzing supply chain risk. Ability to develop and synthesize visualizations to effectively communicate analysis results. Strong programming skills in statistical
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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energy supply systems, multi-objective and stochastic optimization, advanced statistical analysis, and data visualization. This position offers the opportunity to work with a multidisciplinary team of
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-grade algorithms. Demonstrated ability to work on complex data analysis problems and deliver robust computational solutions. Excellent communication skills and a strong interest in interdisciplinary
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, detector operations, and/or upgrade activities Strong skills in scientific programming, software development, and data analysis Ability to work effectively in a collaborative, multidisciplinary research
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will primarily involve organic synthesis lab work and analysis of new materials. Previous experience with electrochemical analysis or battery materials testing is a bonus. The candidate will design or
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materials. This position will primarily involve organic synthesis lab work and analysis of new materials, with some supplemental electrochemical testing. Previous experience with electrochemical analysis
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years) in field of Physics, Astronomy, or Quantum Engineering Considerable analytical skills are necessary to develop and improve techniques for data analysis Demonstrated problem-solving skills and