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operations of the UT-Battelle Operations Committee. Represent ORNL in high-level discussions with DOE-SC and peer laboratories on institutional performance matters. Facilitate executive forums and strategic
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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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Requisition Id 15192 Overview: We are seeking a dynamic and innovative Scientific Lead for developing Quantum-Accelerated High-Performance Computing (QHPC) capabilities. This role includes leading
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professionals to accelerate scientific discovery and engineering advances across a broad range of disciplines. As an important part of the broader High-Performance Computing (HPC) infrastructure, the division
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technologies for large-scale high-performance computing (HPC) and quantum computing (QC) systems. The candidate will be able to contribute to one or more of the following areas: AI/ML, large-scale applications
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for fundamental understanding of materials structure, composition, and behavior. Staff members in eMMA have expertise in high-resolution structural imaging via scanning transmission electron microscopy (STEM
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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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collaborators, and subcontractors to support high quality research across a number of projects. Major Duties/Responsibilities: Serve as a technical expert to independently plan, perform and oversee laboratory
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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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. Perform cryomodule repair and maintenance activities. Participate in the cryomodule refurbishment program restoring original cryomodules to a specified performance. Respond to alarms and abnormal events