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models for high-temperature structural materials with applications in nuclear reactors and other energy systems. The candidate will collaborate with ANL staff to review, validate, and enhance methods
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with internal and external research partners to advance project objectives and meet program milestones Analyze experimental data and prepare reports, technical presentations, and updates for internal
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. The candidate will work closely with computational modeling collaborators to validate reactor designs and optimize operating parameters. The candidate will be expected to contribute to report preparation
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High Energy Physics Division, the successful candidate will contribute to the design, fabrication, and characterization of superconducting devices based on microstrip-coupled TES technology, with
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. This exciting project focuses on further development of beam position monitoring structures and high gradient testing of components. They will play a key role in designing, fabricating, and testing advanced
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of critical elements at mineral-water interfaces Perform extensive synchrotron scattering and imaging experiments at the Advanced Photon Source Develop and apply in situ characterization approaches and, where
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collaboration and innovation. Position Requirements This level of knowledge is typically achieved through a formal education in Statistics, Machine Learning, Computer Science, Logistics/Supply Chain, or a related
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-completed Ph.D. within the last 0-5 years in Atmospheric Science, Meteorology, Climate Science, Applied Mathematics, Data Science, or a related field with strong quantitative and computational research
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is supported by a DOE-funded research program on ultrafast science involving Argonne National Laboratory, University of Washington, and MIT. The goal of this research program is to understand and
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be