55 assistant-professor-computer-science Postdoctoral positions at Oak Ridge National Laboratory
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for Computational Sciences, Computing and Computational Sciences Directorate at Oak Ridge National Laboratory (ORNL). This position offers an exciting opportunity to contribute to research in nuclear theory using
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complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section of the Computer Science and Mathematics (CSM) Division. CSM delivers fundamental and applied research
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analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section of the Computer Science and Mathematics (CSM) Division. CSM
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of the Computer Science and Mathematics (CSM) Division. CSM delivers fundamental and applied research capabilities in a wide range of areas, including applied mathematics and computer science, experimental computing
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applications. You’ll help design, train, and evaluate AI systems that plan, reason, and take actions to accelerate scientific discovery across domains (materials, chemistry, climate, fusion, biology, and more
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Qualifications: Ph.D. in electrical engineering, computer science, or related discipline completed within the last five years. Demonstrated expertise in computed tomography (CT), with experience in sparse-view and
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Systems Research Section/Workflow Systems Group within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher with expertise in data
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Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division, Computing and Computational Sciences Directorate, at Oak Ridge National Laboratory (ORNL). This position presents
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analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division, Computing and Computational
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Postdoctoral Research Associate in the areas Artificial Intelligence (AI) for Integrated Hydrology Modeling. The successful candidate will have a strong background in computational science, data analysis, and