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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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research. The position plays a central role in strengthening the CNM user science program, with a particular focus on electron microscopy and synchrotron-based X-ray microscopy at the Advanced Photon Source
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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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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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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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and program managers. Position Requirements Minimum Education / Experience Requirements: A Ph.D. in physics, applied physics, electrical engineering, or related field. Additional Requirements: Normal
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symmetries, and nuclear data. LER also plays a critical role for the ATLAS National User Facility, where it provides support for ATLAS Users, conducts its own research program, and develops and operates
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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models for microelectronics materials Curate, manage, and integrate heterogeneous datasets from experiments and simulations Collaborate closely with experimental teams to benchmark and refine computational
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experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy