59 high-performance-quantum-computing "https:" "Simons Foundation" Postdoctoral positions at Oak Ridge National Laboratory
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interfaces in quantum materials or energy materials. Advanced electron microscopy capabilities, such as 4D-STEM and high-resolution electron energy-loss spectroscopy (EELS), will be integrated with in situ
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network of universities, national laboratories, and industrial partners to develop a scientific ecosystem for fault-tolerant quantum-accelerated high-performance computing (QHPC). Major Duties
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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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materials that may serve as model systems displaying quantum behaviors. It will also provide opportunities for collaboration with quantum computing efforts within the Quantum Science Center, guiding and
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security. We utilize our expertise in numerical discretization techniques, high performance computing, mesh generation, and geometry representation for a wide variety of physics applications. Our intention
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simulation software related to radiation transport and computational fluid dynamics. Conduct performance profiling of existing scientific software to identify bottlenecks and implement strategies for improving
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE2 [#27206] Position Title: Position Location: Oak Ridge, Tennessee 37831
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Requisition Id 16104 Overview: The Quantum Heterostructures Group is seeking outstanding candidates for a postdoctoral position in quantum information science. You will conduct experimental
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Research Associate to develop and apply computational technique for advanced manufacturing using high-performance computing resources. ORNL’s CCP conduct world-leading research and development in multi-scale
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and