56 parallel-computing-numerical-methods-"https:" Postdoctoral positions at Oak Ridge National Laboratory in United States
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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HPC research within past five years. Preferred Qualifications: The ability to work independently and develop and deploy methods at scale. Experience in high-performance computing and software
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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parallel computing. Demonstrated hands-on experience and understanding of developing scientific data management, workflows and resource management problems. Strong problem-solving and communication skills
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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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of advanced materials. Research efforts will include the application of density functional theory packages and in-house codes, and the development of supplemental numerical tools, to describe
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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Requisition Id 15815 Overview: The Workflows and Ecosystem Services (WES) group under the Advanced Technology Section (ATS) of the National Center for Computational Sciences (NCCS) is seeking a
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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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‑correction, calibration, and adaptive data‑acquisition methods to improve measurement efficiency and throughput Apply physics‑based or computational transport modeling to interpret internal material gradients