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support the development of design rules for engineering microbes for energy and environmental applications. ORNL’s multidisciplinary research environment brings together expertise across biology
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will design and implement differential privacy solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing
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) methods for modeling and optimization of metallic materials and advanced manufacturing processes. Participate in the design of integrated, scalable numerical methods and uncertainty quantification. Follow
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/Responsibilities: The successful candidate will be responsible for conducting, coordinating, and reporting complex research assignments related to energy conversion systems including: Design and develop experimental
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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single node between multiple secure workloads. Investigate and evaluate mechanisms for secure encrypted communication across RDMA based networks. Design and evaluate key distribution and management
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Laboratory (ORNL) and you will join our team as part of the Department of Energy’s Nanoscale Science Research Center MEERCAT project. As part of our research team, you will investigate excitonic and electronic
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National Laboratory (ORNL). Major Duties/Responsibilities: Interacting with a team of experts at ORNL on projects evaluating the U.S. Department of Energy Weatherization Assistance Program and its efficacy
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/Responsibilities: Participate in the design, implementation, and deployment of scalable AI/DL models for power grid systems, including surrogate models and foundation-model workflows for OPF and related grid
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a variety of projects involving internal and external dosimetry and radiation risk analysis The CRPK provides technical assistance to U.S. federal agencies involved in development of federal guidance