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of irradiated ceramics and alloys for tritium technology development using advanced experimental and computational methods. The researcher will perform characterization of model systems using techniques such as
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Postdoctoral Research Associate - Multifunctional Equipment Integration Energy Conversion Technology
system technologies. This position resides in the Multifunctional Equipment Integration Group in the Thermal System Science Research Section, Buildings and Transportation Science Division, Energy Science
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Requisition Id 16974 Overview: We are seeking a Postdoctoral Research Associate to conduct Mechanical Behavior of Advanced Structural Alloys by Engineering Diffraction. This position resides in
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construction. With the support from the U.S. DOE Building Technologies and Industrial Technologies Offices we have the most extensive building envelope research portfolio and manufacturing demonstration
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mechanical testing. This knowledge will be used to assess the suitability of these materials for nuclear and space applications. This position resides in the Materials Processing Group within the Materials
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challenges facing the nation. The Isotope Applications Research (IAR) group is a multi-disciplinary team of scientists and engineers working to advance technologies and knowledge for the production and
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to ongoing fuel cycle material-related research projects Communicate and coordinate with technical staff members to execute work within shared laboratory spaces Contribute to proposal and focus area
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algorithms at scale on ORNL's computational resources, including the Frontier supercomputer, addressing critical challenges in science and engineering. Communicate and coordinate experimental results with
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Postdoctoral Research Associate to join the Microbial Engineering Group. In this role, you will develop next‑generation genetic tools for non‑model microorganisms, enabling precise genome engineering in
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field experimental ecophysiology measurements Experience applying ML/AI to biological or environmental data (e.g., transformer, state space models, multi-layer perceptrons, convolutional neural networks