84 electrical-machine-design-"https:" "https:" "https:" "https:" "https:" "https:" Postdoctoral positions at Oak Ridge National Laboratory
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artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine learning, high
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, irradiation experiments, and rapid design/fabrication. As part of the research team, you will investigate optical fiber instrumentation. More specifically, this position would develop Fabry-Perot interferometer
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Demonstrated experience in control theory and control system design (e.g., model predictive control, feedback control, adaptive control) Experience applying machine learning/AI to engineering systems Proficiency
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committed team of scientists to develop composite pipe technologies for geothermal energy or similar harsh environment applications. Design, develop, fabricate, and test composite material in lab scale and
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/Responsibilities: Design, assemble, align, and characterize optical instrumentation for squeezed-light sensing and microscopy. Benchmark quantum-enhanced measurements against coherent-light measurements
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transport models to assess the production and transport of in-vessel impurities in MPEX operating scenarios. Perform predictive modeling to inform the design of new MPEX target geometries, with a focus to
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with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate
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mechanical engineering, material science, electrical engineering, computer engineering, computer science, data science, applied mathematics, or a closely related field Demonstrated experience with multimodal
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photonic quantum sensing and computing. Experience with control electronics, data acquisition systems, machine learning and AI for control and optimization of experimental apparatus. Experience with vacuum
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI