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platforms, data management portals, data processing pipelines, API gateways, and persistent services for the entire data lifecycle on our on-premises Kubernetes clusters, with a strong focus on scalability
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-intensive processing of data originating at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable
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solid materials within the uranium fuel cycle. Research topic areas will be related to understanding the effects of material processing on chemical and physical observables and translating this
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scale with a particular focus on AI for Science, Natural Language Processing (NLP), and AI for Operations. Our aim is to push the boundaries of computational science by integrating advanced AI methods
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data-intensive processing of data originating at scientific edge systems using large-scale HPC/AI computational and storage systems. Coauthor peer-reviewed publications, technical reports, and
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, separations chemistry, theory, spectroscopy, and materials characterization. This project will develop materials and processing technologies that will result in a transformational improvement in separation
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software tools for integrated system dynamic and transient simulations. Integrate post-processing measures for simulations to help with automation. Deliver ORNL’s mission by aligning behaviors, priorities
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. natural gas production, processing, transmission, storage, or distribution infrastructure. Knowledge of natural gas system operations and gas-electric system interdependencies. Experience with
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, restart, and post-processing pipelines. Author peer reviewed papers for journals and conferences, technical reports, open-source software, and represent the organization by making technical presentations
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radiation damage mechanisms and experience in the processing, testing, and characterization of ceramic materials. This position resides within the Radiation Effects and Microstructural Analysis Group (REMAG