43 lecturer-in-computer-science Postdoctoral research jobs at Oak Ridge National Laboratory
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Requisition Id 14889 Overview: We are seeking a Postdoctoral Research Associate who will focus on delivering groundbreaking computational chemical and materials science at the forefront
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Requisition Id 14702 Overview: Oak Ridge National Laboratory is the largest US Department of Energy (DOE) science and energy laboratory, conducting basic and applied research to deliver
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Requisition Id 15028 Overview: We are seeking a Postdoctoral Research Associate – Chemical Engineering to support research activities within the Isotope Applications Research Group (IARG) at Oak
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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A Ph.D. in evolutionary biology, ecology, plant biology, genomics, bioinformatics, computer science, plant
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success. Basic Qualifications: A PhD in theoretical or computational chemistry or closely related field in physical chemistry or chemical physics completed within the last 5 years. Demonstrated expertise
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Requisition Id 14862 Overview: Oak Ridge National Laboratory (ORNL) is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver solutions
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Requisition Id 15217 Overview: The Multiscale Biomedical Systems group in the Advanced Computing in Health (ACH) section in Computational Sciences and Engineering Division (CSED), Computing and
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. Supporting the largest radioisotope production and research portfolio within the Department of Energy (DOE) Office of Science for Isotope R&D and Production, as well as extensive isotope production programs
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at the intersection of quantum information science and fundamental materials physics. The research program focuses on understanding the fundamental limits of spin-based quantum sensors as probes of magnetic and
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, transportation, and more, with a special emphasis on grid resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science