53 formal-verification-computer-science Postdoctoral positions 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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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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skills to bear as you develop new methods to address scientific and engineering problems, collaborate with leaders in your field and across the laboratory, while working with the world’s fastest computers
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-task efforts in molecular biology, NMR, dynamic nuclear polarization (DNP) and macromolecular crystallography, and collaboration with computational scientists developing ML and AI tools for molecular
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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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. 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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manufacturing (AM) processes. This position resides in the Computational Sciences and Engineering Division (CSED) at Oak Ridge National Laboratory (ORNL). CSED focuses on transdisciplinary computational science
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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