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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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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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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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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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to accelerator operations. Experience working with complex algorithms and data-driven models. Experience using high-performance computing clusters for simulation and data analysis. Ability to model Argonne’s core
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for critical energy and technology sectors. Ability to assess the economic and operational impacts of large-scale AI adoption (e.g., data centers, compute infrastructure) on U.S. electricity demand, generation
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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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Argonne National Laboratory invites applications for a postdoctoral research position in experimental physics, with a focus on advancing superconducting particle detector technology for next
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lead a world-class research program that aligns with DOE priorities in low-energy nuclear physics outlined in the 2023 Nuclear Science Advisory Committee Long Range Plan for Nuclear Science. Additional
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capable of hypothesis generation, experimental design, data analysis, and iterative learning in biological contexts. This project seeks to transform how computational and experimental biology research is