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computational materials science aligned with CNM strategic themes and the DOE mission Publish in refereed journals and present at conferences, symposia, and seminars Contribute to proposal development and assist
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an independently-funded research program within 2-3 years. The successful candidate will develop and apply advanced data-driven methodologies to accelerate discovery in materials/chemistry design, characterization
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of research in computational materials science and/or AI/ML, with demonstrated ability to collaborate effectively with experimental researchers and to impact experimentally driven programs Demonstrated ability
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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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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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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