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and Eligibility Applications will be accepted from January 7, 2026, March 1, 2026, for one position starting as early as May 4, 2026. This position will support one postdoc for two years. You must first
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properties of the above materials. Collaborate with ORNL postdocs and staff who are involved in structural characterization. Participate in the development of new ideas and projects. Present and report
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mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design
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relevance to clean energy, climate resilience, and infrastructure planning. Postdocs benefit from access to world-leading high-performance computing facilities and a deeply interdisciplinary research
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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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-year residency requirement, you will be required to obtain a PIV credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date
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learning conferences and journals. Be a part of a collaborative research environment which will provide the opportunity to perform cutting-edge research in deep learning and scientific computing. Deliver
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from plant genomics to phenomics with biological mechanisms embedded in deep neutral networks. GPTgp will allow task-specific training and transfer learning across reactions, pathways, biodesigns, and
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engineering, architecture, architectural engineering, or related field completed within the last five years. Experience in building energy modeling and analysis. Deep understanding of building thermal physics
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credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting