61 parallel-computing-numerical-methods "Fields Institute" Postdoctoral positions at Oak Ridge National Laboratory
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expertise in multiscale and multifidelity simulation techniques ( ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or
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-Performance Computing (HPC), scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature strengths in high-performance
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Requisition Id 16562 Overview: The Analytics and AI Methods at Scale (AAIMS) group at the National Center of Computational Science (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking
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and methods development in areas such as reactor core physics, nuclear fuel cycle assessments, radionuclide inventories, and source terms. The major customers of the work performed in this group are
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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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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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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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Monte Carlo radiation transport software and applications Experience with numerical computing using programming languages such as C++, Python, etc Author or co-author of open literature publications
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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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imaging of cells and tissues. This position resides in the Quantum Computing and Sensing Group in the Computational Science and Engineering Division (CSED), Computing and Computational Sciences