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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
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field. Hands-on experience with free-space optical alignment, THz beam delivery, electro-optic sampling, polarization optics and imaging, or time-resolved pump-probe experiments. Proficiency in Python
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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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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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or image processing Experience with AI-assisted or feedback-driven fabrication workflows Interest in quantum photonic platforms, electro-optic systems, or light–matter coupling physics Application Materials
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scientists both within and beyond CNM. Key Responsibilities Develop and apply advanced S/TEM techniques, focusing on atomic-resolution imaging and spectroscopy (EELS/EDS) Implement automation and high
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involvement in three SciDAC-5 projects: 1) Femtoscale Imaging of Nuclei using Exascale Platforms, 2) Fundamental nuclear physics at exascale and beyond, and 3) Nuclear Computational Low Energy Initiative
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synchrotron-based techniques to inform process development. The role requires a strong background in synchrotron characterization techniques, mainly three-dimensional imaging (microtomography and
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, at scale. It will help us better understand and improve DAOS to meet the needs of AI-driven science applications. We expect the postdoc to help prototype, benchmark, and evaluate strategies to better support
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spectroscopy, as well as experience with high resolution STEM imaging • Excellent written and oral communication skills Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed