27 high-performance-computing Postdoctoral positions at Oak Ridge National Laboratory
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the following areas are required: 1) wetland science; 2) hurricane science; 3) remote sensing; 4) deep learning and AI, 5) high-performance computing. Experience using AI models is required; experience
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safety at ORNL and DOE sites. This position resides in the Performance Engineering group in the Data and AI Systems Section in Computer Science and Mathematics division within Computing and Computational
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binding and selectivity manifested in improved separations. Develop and evaluate statistical mechanical and machine learning tools for studying molten salts using the leadership-class high-performance
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computing AI on High-Performance Computing (HPC) cluster. Examples on areas of research interest include but are not limited to: Vision transformers. AI foundation models. Computing and energy-efficient
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to support the design. Perform computational fluid dynamic, computational magnetohydrodynamic, thermal hydraulic, and mass transport simulations of tritium breeding blanket designs. Collaboration with a broad
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, the candidate is also expected to be involved with both basic and applied research projects such as the synthesis of surface initiated homopolymers, block copolymers and dynamic polymers, recyclable high
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, Computing and Computational Sciences Directorate, at Oak Ridge National Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) and machine
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Requisition Id 15276 Overview: We are seeking a postdoctoral research associate who will study the dynamics of high-intensity proton beams in the Spallation Neutron Source (SNS) ring. This project
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support the Plutonium-238 Supply Program at ORNL that is responsible for producing plutonium-238 for NASA in support of powering deep space missions. Major Duties/Rsponsibilities: Perform experimental and
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at the intersection of quantum information science and fundamental materials physics. The research program focuses on understanding the fundamental limits of spin-based quantum sensors as probes of magnetic and