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studies (e.g., EELS, EDS) to probe defect structures and dynamics Apply advanced image processing and analysis; develop AI/ML workflows for quantitative defect characterization Implement high-throughput and
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. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments. Experiments with Argonne involvement include, but are not
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-generation nuclear science experiments at Jefferson Lab and the Electron-Ion Collider (EIC). As part of our growing multidisciplinary team, you will contribute to the development of superconducting nanowire
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Microscopy (LTEM) studies of magnetic heterostructures. We are interested in using real space imaging techniques to understand the evolution of energy landscapes that lead to emergent domain behavior in