274 quantum-engineering-"https:"-"https:"-"https:"-"Univ" positions at Oak Ridge National Laboratory
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Requisition Id 15711 Active DOE Q, active DOD Top Secret, or active DOD TS/SCI clearance is required for consideration. Overview: We’re hiring a Full Stack Information Technology System Engineer
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Qualifications: An M.S. in electrical engineering with a focus on power electronics applications for the grid Experience related to designing and controlling power electronic systems. Experience with power
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challenges facing the nation. We are seeking a Postdoctoral Research Associate who will support the Quantum Sensing and Computing Group in the Computational Science and Engineering Division (CSED), Computing
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Requisition Id 15669 Overview: We’re hiring an AI Developer having AI/ML development and deployment skills! The position and its focus are to design, develop and move to production AI/ML tools and
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funded by the U.S. Department of Energy (DOE) Office of Basic Energy Sciences (BES) in the Materials Sciences and Technology Division (MSTD). The successful candidate will be expected to work effectively
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Requisition Id 15528 Overview: The Laboratory Protection Division is seeking a qualified Security Network Engineer. In this role, you'll ensure the stability, performance, and secure operation of
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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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Requisition Id 15882 Overview: We are seeking a Research Staff Scientist to advance our fundamental understanding in 2D materials, thin films, and hybrid nanostructures for quantum information
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challenges facing the nation. We are seeking a Postdoctoral Research Associate who will support the Quantum Sensing and Computing Group in the Computational Science and Engineering Division (CSED), Computing
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security challenges facing the nation. We are seeking a Machine Learning (ML) Research Engineer who will support the development of self-supervised learning methods for large vision-language models