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. This position is part of the DOE-BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC), a multi-facility collaboration integrating experimental measurements, simulations, and data science to
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autonomous experimental campaigns, this position is suited for a highly energetic and self-driven researcher willing to work in highly collaborative teams. This position will involve a considerable amount of
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Coherent Diffraction Imaging (BCDI), ptychography, and X-ray Photon Correlation Spectroscopy (XPCS). The goal is to move beyond simple correlations to discover the causal, governing rules of defect-property
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the AI system with beamline control systems (e.g., EPICS) to close the autonomous loop. The position requires publishing results in high-impact journals, presenting at international conferences, and
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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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resources—including the Autonomous Sputter Beam Epitaxy system, the QuEEN-M electron microscopy platform, and synchrotron X-ray beamlines at the Advanced Photon Source. This is an outstanding opportunity