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to the development of scalable and efficient implementations of these algorithms for state-of-the-art high performance computing facilities. Collaborate within a multi-disciplinary research environment consisting
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optimization and consensus algorithms. Knowledge of large models and hyper-parameter optimization. Knowledge of high-performance computing and its applications. An excellent record of productive and creative
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frameworks such as Qiskit, CUDA-Q, PennyLane, Cirq, or equivalent platforms, and knowledge of quantum algorithms, quantum error correction, fault-tolerant computing, or quantum resource estimation. Experience
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and porting of scalable science applications and algorithms for OLCF and other leadership computers. Debugging and tuning applications for high performance. Developing performance models of existing
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five-year mission of the ORNL Quantum Science Center (QSC) to establish a quantum-accelerated computing ecosystem for scientific applications. The successful candidate will develop, test, and evaluate
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Requisition Id 16262 Overview: We are seeking a postdoctoral researcher to work at the intersection of tensor networks, quantum algorithms, scientific computing, topological physics, and quantum
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platform. The successful candidate will (1) integrate heterogeneous sensors and onboard computing hardware; (2) develop methods for LiDAR-based simultaneous localization and mapping (SLAM), autonomous
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computational models, systems, and AI/ML tools using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. In
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Computing Methods for Physical Sciences Section in CSED. The MsM group is focused on delivering multiscale, multi-fidelity computational models and systems using algorithms and analytics for materials and
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algorithms at scale on ORNL's computational resources, including the Frontier supercomputer, addressing critical challenges in science and engineering. Communicate and coordinate experimental results with