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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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chemistry, chemical engineering, physics, computational science, materials science, or related field. Background in synchrotron characterization techniques. Experience collecting and analyzing large
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to the development of new research directions aligned with program goals. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in Chemical Engineering, Materials
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Argonne National Laboratory invites applications for postdoctoral research positions in experimental physics, with a focus on advancing superconducting particle detector technology for next
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The Chemical Sciences and Engineering Division invites you to apply to a postdoctoral appointee opening. The successful candidate will perform research in the Interfacial Processes Group
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and processing strategies aimed at achieving high performance, cost-effectiveness, and manufacturability. The selected candidate will leverage the capabilities of the Materials Engineering Research
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, in Electrical Engineering and Computer Science or related field obtained within the last five years. Experience with X-ray physics or optical wave modeling. Proficiency in programming with Python
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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for such models include high-resolution 3D imaging, time-resolved materials characterization, and atomic structure determination. Scientific instrument data is often multimodal in nature and developing DL models