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. Experience applying computer vision, image analysis, and/or machine-learning methods to microscopy or materials characterization data. Demonstrated ability to analyze microstructural data and relate
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) at Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied
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bench scale micro-computed tomography and ultrasonic sensing methods to evaluate the state of charge and state of health of iron- and lead-based electrodes. Your research will be complemented by studies
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technology research and development by fabricating prototype electrodes and pouch cells in a dry-room environment and by contributing to experimental design, data interpretation, and technical reporting
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together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for multimodal federated learning
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implementing BCDI, XPCS, nano-beam and time resolved microscopy, PDF, 3D imaging techniques, PCI, and other techniques for advanced characterization of materials across solid-liquid and into melt. Measurements
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will receive full consideration. Key Responsibilities AI-ready data and analysis for the ePIC Barrel Imaging Calorimeter and our Jefferson Lab program Support for the PRad-II and X17 experiments
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fluorescence tomography at beamline 2-ID-E and 2-ID-D with focus on bioimaging of soil aggregates, as well as computational framework for modeling and reconstruction of related 3D datasets. The successful
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perform advanced synchrotron experiments to probe structural, chemical, and dynamic evolution of defects in thin films and heterostructures. Utilize techniques such as Bragg coherent diffraction imaging
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-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human–computer interaction (HCI), and high-performance computing