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capabilities for advanced nuclear materials systems. In addition, this work includes developing processes that connect mechanical and thermophysical testing data with the microstructures of ceramic and metallic
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, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
with the Multiscale Dynamics and Heterogeneities in Quantum Materials themes at the CNMS and US DOE’s Genesis projects. The candidate is expected to work closely with Soumendu Bagchi and P. Ganesh. As
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Requisition Id 15452 Overview: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges
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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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in ORNL’s Center for Radiation Protection Knowledge (CRPK). The candidate will work with experts in computational radiation dosimetry and risk assessment. The candidate should be an independent thinker
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, Research Accelerator Division, Neutron Sciences Directorate at Oak Ridge National Laboratory (ORNL). The successful candidate will work closely with SNS research and operations staff to design and carry
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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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in high-performance computing and data analytics with applications in a large variety of science domains and NCCS is home to some of the fastest supercomputers and storage systems in the world
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy