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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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characterization, and machine learning. The role offers the opportunity to leverage Argonne’s world-class scientific capabilities and engage with a strong network of internal and industry collaborators. Position
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demonstrates a professional attitude. Skilled written and verbal communicator, including the ability to present complex information so that it is understandable to a broad audience. Strong computer skills
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Manufacturing group perform science-based membrane synthesis and scaleup development by using roll-to-roll manufacturing and machine learning enabled in-line characterization and quality control methods
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platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will
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-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human–computer interaction (HCI), and high-performance computing
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field Experience leveraging artificial intelligence or machine learning in the development of battery electrolytes and catalyst materials Demonstrated expertise in lithium–sulfur battery materials and
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an integrated framework to explore advanced workloads including simulations with in-situ visualization and, possibly, machine learning integration. This work will inform future ALCF platform procurement decisions