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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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) in the field of accelerator physics or a closely related science and engineering discipline Strong experience developing and applying computational modeling and simulation Familiarity with accelerator
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limited to, ATLAS at CERN, the South Pole Telescope, and the Simons Observatory. The candidate is also expected to work closely with computational experts at the Computational Science (CPS) division
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Extensive knowledge of Microsoft Excel and good computer programming skills Knowledge of techno-economic analysis and life cycle analysis Experience working with Argonne’s EverBatt model, GREET model, and
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The Surface Scattering and Microdiffraction (SSM) group in the X-ray Science Division (XSD) at the Advanced Photon Source (APS), Argonne National Laboratory is seeking Two Postdoctoral Appointees, both focused on multimodal synchrotron characterization of defects and interfaces in oxides and 2D...
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analysis Interdisciplinary Collaboration - Experience working in cross functional teams including molecular biologists, chemists, radiation experts and computational biologists Core Values - Ability to model
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samples and characterize dynamic behaviors. The candidate will be part of a highly collaborative team and actively interact with other groups, including optics, computation, and time-resolved research
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Hands-on experience with two-dimensional materials modeling Proficiency in database development and management for computational materials data Strong programming skills and experience with software
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Knowledge of in RNA biology Experience with RNA CryoEM/crystallography/SAXS Prior experience with high-throughput or computational protein design/screening techniques Job Family Postdoctoral Job Profile
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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