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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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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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candidate will develop a novel multiscale multimodal experimental apparatus with precise control during data acquisition, as well as work on data processing pipeline, adaptable to imaging of energy-conversion
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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
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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
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thin film deposition is preferred. Advanced image processing and analysis skills. Experience with micromagnetic simulation is preferred. Ability to work independently as well as in collaboration with a