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Requisition Id 14630 Overview: We are seeking a Postdoctoral Research Associate to work on groundbreaking research focused on techniques for on-the-fly analysis of neutron diffraction data. Oak
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 12 hours ago
Engineering stresses the application of science and engineering, mathematical analysis, and computer techniques to biomedical problems. The research and entrepreneurship of the faculty, students, and staff aim
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Organization National Energy Technology Laboratory (NETL) Reference Code NETL-Postdoc How to Apply A complete application consists of: An application, including academic history, work history
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byproducts from used fuel reprocessing and recycling to stable and reusable forms. The work will involve wet chemistry, analytical characterization techniques, and flow sheet development. The FCCT Research
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(transportation, farming, health, etc.). Therefore, WSNs/IoT have received enormous attention where various communications-related performance metrics were considered, such as reliability, connectivity, throughput
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of correlated and topological materials. Work in thin film synthesis and physical property characterizations. Experience in user facilities. Complete data analysis of structural, electronic, magnetic, and
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Apply now to Postdoc-UCSB Physics Dept. (Galaxy Evolution)/Casey Research Group Job #JPF02904 Physics / College of Letters & Science - Mathematical, Life, and Physical Sciences / UC Santa Barbara Position
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. Additional skills for Image Science: Programming, careful data analysis, high-performance computing, shell scripting (c, bash), medical imaging Candidates with a recent PhD or those anticipating Ph.D. (all
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, proof assistants, term rewriting systems, optimizing compilers, program analysis tools, constraint solvers, or similar. Familiarity with linear algebra libraries and high-performance computing
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description