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structural models and compute electronic and vibrational properties. Develop and train neural-network or other machine-learned interatomic potentials to enable large-scale molecular dynamics (MD) simulations
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automation, high-throughput data acquisition, and real-time data processing, offering a unique opportunity to advance S/TEM capabilities, publish impactful research, and collaborate with a diverse group of
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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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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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imaging surveys Experience with computational astrophysics, including Python-based data analysis workflows Appointment Details The position is available beginning June 1, 2026, or earlier by mutual
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math, HPC, signal processing, computational physics and materials science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of
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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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., optical coherence tomography and X-ray imaging). The project will include exploring new membrane materials, exploring degradation mitigation strategies, and guiding next-generation membrane design for
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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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data analysis/spectral image processing. Use of data analytics or machine learning to guide process design and optimization. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long