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Associate position for health research projects. The research activities include HIPAA compliant research data that has been entrusted to ORNL by sponsors such as the National Cancer Institute (NCI) and the
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United States respond to environmental disturbances. This position resides in the Watershed Systems Modeling (WSM) group in the Environmental Sciences Division (ESD), Oak Ridge National Laboratory (ORNL). ESD is
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for energy and bio-inspired processes and molten salts chemistry for nuclear-reactor applications. This position resides in the Carbon and Composites Group in the Separations and Polymer Section, Chemical
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phenomena of nuclear reactor fuel cladding and core internal materials. Strong materials characterization skills are required. This position resides in the Fuel Cladding and Core Internals (FCCI) Group in
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. This position resides in the Soft Materials and Membranes Group in the Chemical Sciences Division, Physical Sciences Directorate (PSD), at Oak Ridge National Laboratory (ORNL). As part of our team, you will work
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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in evolutionary biology, plant biology, genomics, bioinformatics, mathematics, statistics, computer
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for microreactors. This position resides in the Nuclear and Extreme Environment Measurements (NEEM) Group in the Nuclear Energy and Fuel Cycle Division, Fusion and Fission Energy and Science Directorate at Oak Ridge
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visual representation and 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
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: To be eligible you must have completed a PhD in materials science, chemistry, physics, engineering, or a related field with in the last 5 years. Visa sponsorship is not available for this position
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development. Basic Qualifications: A PhD in computer science/engineering, electrical engineering, data science or a related field completed within the last five years. Experience of AI and efficient computing