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Peatland Responses Under Changing Environments) experiment and other DOE-supported observational networks. Major Duties/Responsibilities: Develop, implement, and test new and improved process representations
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mechanical testing to uncover processing–microstructure–property relationships. The candidate will have opportunities to interact with multidisciplinary teams and contribute to high-impact publications and
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five-year mission of the ORNL Quantum Science Center (QSC) to establish a quantum-accelerated computing ecosystem for scientific applications. The successful candidate will develop, test, and evaluate
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of analytical characterization techniques and performance testing of cementitious materials. Knowledge on durability evaluation of cementitious materials. Experience setting and conducting laboratory experiments
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Postdoctoral Research Associate - Multifunctional Equipment Integration Energy Conversion Technology
research assignments related to energy conversion systems including: Prototype development Material synthesis and analysis Performance testing Analysis of results Preparing research publications Prepare
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workflows. Preferred Qualifications: Hands-on experience with machine tool metrology and performance evaluation methods such as ball-bar testing, laser interferometry, volumetric accuracy assessment, spindle
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responses to warming, drought, and elevated CO2 (e.g., gas exchange, fluorescence, hydraulics, respiration, water potential, thermal tolerance) Trait synthesis at scale (e.g., using trait databases TRY, FRED
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committed team of scientists to develop composite pipe technologies for geothermal energy or similar harsh environment applications. Design, develop, fabricate, and test composite material in lab scale and
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deformation, strengthening, and failure in advanced alloys. The research will focus on integrating microstructure characterization, in situ diffraction techniques, and mechanical testing to uncover processing
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multidisciplinary team of hydrologists, Earth scientists, and computational scientists to leverage leadership-class computing resources for large-scale model training, testing, and deployment. Contribute