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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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The Chemical Sciences and Engineering Division at Argonne National Laboratory invites applications for a Postdoctoral Appointee to join the Aqueous Battery Laboratory. This is an exciting
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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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Physics, Chemistry, Material Science, Geosciences or Engineering. Demonstrated experience with synchrotron x-ray techniques such as Bragg Coherent Diffraction Imaging (BCDI), X-ray Photon Correlation
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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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information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum