28 data-scientist Postdoctoral positions at Oak Ridge National Laboratory in postdoctoral
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for geothermal casing related harsh environments applications. A background in polymer chemistry research or related fields, composite material development, material science, and data analysis is preferred. Strong
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models of gas transport and process behavior in industrial systems Collaborate with a team of scientists from across the national laboratory complex on modeling efforts Extend process flow modeling across
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and engineers, leveraging cutting-edge resources; most notably the Frontier
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within a multi-disciplinary research environment consisting of computational scientists, computer scientists, electrical engineers, domain scientists, and applied mathematicians conducting basic and
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to accelerate multiscale materials and flow sciences. Major Duties/Responsibilities: Collaborate within a multi-disciplinary research environment consisting of computational scientists, computer scientists
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member in the Scientific Computing Group in the OLCF, and will collaborate with leading computer and computational scientists at ORNL and in the US Lattice QCD community in the development and application
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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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experiments, classical simulations, and quantum simulations. Collaborate with ORNL staff scientists and external collaborators. Write peer-reviewed scientific articles and present research findings at major
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, prioritization of tasks, interpretation of results, troubleshooting, and development of next-step strategies. Collaborate effectively with scientists across disciplines while independently advancing assigned