14 high-performance-quantum-computing Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 16984 Overview: We are seeking a highly motivated Postdoctoral Research Associate to conduct research at the intersection of quantum computing and high-performance computing (HPC
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high-performance computing facilities. There is significant potential for high-impact research contributions at the forefront of computational quantum many-body physics. This position resides within
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(NCCS), Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) to perform leading-edge computational materials research and development. The scientific focus
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(ORNL). CSED performs transdisciplinary computational science and analytics at scale to produce advancements in physical sciences, engineering, and biomedicine and health. The Quantum Computational
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professionals to accelerate scientific discovery and engineering advances across a broad range of disciplines. As an important part of the broader High-Performance Computing (HPC) infrastructure, the division
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. You will perform cutting-edge research on theory and modeling of dynamics in condensed matters. Major Duties/Responsibilities: Development of theoretical framework for driven and open quantum systems
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-body systems and development of new neutron sample environment that allows time-resolved neutron scattering capabilities. This position resides in the Quantum Heterostructures Group in the Foundational
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multi-physics simulations on high performance computing (HPC) and ML Experience working in a multi-disciplinary research environment Special Requirements: Applicants cannot have received their Ph.D
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. Demonstrated programming ability and knowledge of Python and/or C++. Experience with deep learning frameworks like PyTorch and application on high-performance computing (HPC) environments using distributed data
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performance tools Study the performance, resiliency, power, and efficiency of modern and future high-performance computing systems under various workload characteristics through measurement, modeling, and