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Equipment Integration Group in the Building Technology Research Section, Buildings and Transportation Science Division, Energy Science and Technology Directorate at Oak Ridge National Laboratory (ORNL
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data (e.g., engineering diagrams), and predictive modeling Design and implement benchmarking and evaluation frameworks for emerging AI systems, ensuring reproducibility and scientific rigor Build
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environment. They are supported with mentorship, conference travel, and opportunities to develop independent research directions and build professional networks. Oak Ridge and the greater Knoxville area offer
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to ORNL’s world-leading computational resources, including the Frontier supercomputer, and the chance to make meaningful contributions to DOE's mission-critical scientific domains. Roles and responsibilities
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such as federated learning. Provenance and Reproducibility Frameworks: Build systems that enable detailed provenance tracking, schema validation, and auditable workflows to ensure trustworthy and
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modeling techniques and make fundamental contributions to the field. Interact with other researchers, technicians, and students to shape and drive the research agenda. Present and report research results and
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networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes. The position is supported by
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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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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of geothermal wells, reduce costs, improve productivity, and mitigate risks. The project builds upon previous MEERA work focusing on casing and cementing activities that identified the cost of casing, cement and