60 parallel-computing-numerical-methods "DIFFER" Postdoctoral positions at Oak Ridge National Laboratory
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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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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
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Requisition Id 17232 Overview: We are seeking a Postdoctoral Research Associate who will focus on applying computational methods in the areas of reaction chemistry in condensed phase systems
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minimize performance loss while still providing robust privacy guarantees. Develop novel privacy-preservation methods that accommodate the diverse privacy requirements of a large number of clients
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– especially related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials