80 assistive-technology-"https:"-"https:"-"https:" Postdoctoral positions at Oak Ridge National Laboratory
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Qualifications: A PhD in Computational Chemistry, Physics, Chemical Engineering, Materials Science and Engineering, or a related field completed within the last 5 years Experience in theoretical and computational
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, Educational Assistance, Relocation Assistance, and Employee Discounts. This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired. We
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their area, interact with ORNL staff, industry, and academic partners. This position resides in the Sustainable Manufacturing Technologies Group in the Manufacturing Science Division (MSD) of the Energy
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Requisition Id 17011 Overview: The Nuclear Energy and Fuel Cycle Division (NEFCD) of Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to assist in the development
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help scale up the technology for commercial scale applications. Perform development work at vendor’s site that requires travel. Interact with industrial partners, other research organizations, and
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, Educational Assistance, Relocation Assistance, and Employee Discounts. This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired. We
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to research proceedings and publications Prepare technical reports Deliver briefings to stakeholders Basic Qualifications: A PhD in Chemical Engineering, Mechanical Engineering, Physics, Materials Science
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separation science and technologies for energy applications. Support research and program-development activities in advanced separation and purification, including the recovery of critical materials used in
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-assisted model calibration, or pattern detection in large observational datasets) to improve nutrient-cycling representation and reduce predictive uncertainty in models. Collaborate with an interdisciplinary
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-assisted model calibration, or pattern detection in large observational datasets) to improve nutrient-cycling representation and reduce predictive uncertainty in models. Collaborate with an interdisciplinary