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research. The position plays a central role in strengthening the CNM user science program, with a particular focus on electron microscopy and synchrotron-based X-ray microscopy at the Advanced Photon Source
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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have a strong background in fundamental electrochemistry, with preferable hands-on expertise in computational materials science. The applicant should be well versed in code development, application of AI
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/reactions, with increasing emphasis on using artificial intelligence and quantum information science. The group has access to extensive laboratory and national computational resources and has significant
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Recent or soon-to-be completed (typically within the last 0-5 years ) Ph.D. in Computer Science, Electrical Engineering, or a related field. Demonstrated research expertise in AI and machine learning, with
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and contributing to reusable research software when appropriate. Position Requirements Required skills, experience and qualifications: PhD in computer science, applied mathematics, electrical
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multidisciplinary team, the candidate will work at the intersection of AI/ML, domain sciences, and high-performance computing. The role requires a strong foundation in LLMs and machine learning, along with
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primary goal of this work is aimed at advancing next-generation, lithium-ion technology through a detailed understanding and mitigation of surface degradation mechanisms that limit state-of-the-art lithium
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regulations and contract. Skill in modeling, processing, and analyzing computational results to inform accompanying experimental efforts. Skill in the use of modern collaborative coding practices Demonstrated
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symmetries, and nuclear data. LER also plays a critical role for the ATLAS National User Facility, where it provides support for ATLAS Users, conducts its own research program, and develops and operates