57 postdoc-position-semantic-web Postdoctoral research jobs at Oak Ridge National Laboratory
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(ORNL). This position will focus on the development, characterization, and application of engineered nanoparticles for medical isotope systems, including technologies relevant to isotope processing
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Ridge National Laboratory (ORNL) seeks a motivated Postdoctoral Research Associate. This position primarily focuses on large-scale molecular dynamics (MD) simulations and AI-integrated multiscale modeling
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will involve designing beam dynamics experiments, measurement, simulation, and data analysis. This position resides in the Accelerator Physics Group in the Accelerator Science and Technology Section
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the ability to work independently and to participate creatively in collaborative teams across the laboratory. Ability to function well in a fast-paced research environment, set priorities to accomplish multiple
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transformative solutions to compelling problems in energy and security. We are seeking a Postdoctoral Research Associate to perform experimental studies on chirality driven quantum states. This position resides in
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. This position resides in the Quantum Heterostructures Group in the Foundational & Quantum Materials Science Section, Materials Science and Technology Division, Physical Sciences Directorate at Oak Ridge National
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding. Please submit two letters of reference when applying to this position
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in collaborative teams across the laboratory Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing
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) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and