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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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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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processes Implement models and perform numerical simulations Analyze and interpret simulation and experimental data Integrate AI tools and workflows to enhance modeling and accelerate learning Collaborate
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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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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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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will require flexibility and a willingness to learn new techniques and approaches. In addition, there may be overnight experiments being run unattended, the candidate must be able to respond to issues in
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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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to analytical techniques for characterizing electrolytes using UV-VIs absorption spectroscopy, ICP-MS, LC-MS, GC-MS, and ICP-MS. This position will include learning experimental workflows and adapting them
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cells and electrolyzers is welcomed. Experience with statistical analysis methods such as PLS-DA, supervised learning and database building are highly encouraged. The applicant is expected to think and