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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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for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of new computational techniques specifically
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computational foundations of that capability and help bridge the gap between Bayes theory and practical application: knowledge integration, developing robust likelihood frameworks, sampler behavior for long
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for collaborative use within the EGRET project. Work closely with remote-sensing scientists, hydrologists, environmental scientists, process-based modelers, and computational scientists across DOE laboratories and
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as
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key findings in peer-reviewed journals in a timely manner. Collaborate within a multi-disciplinary research environment consisting of computational scientists, experimentalists, and engineers conducting
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(ORNL) but will involve close (daily) collaborations with scientists/team members across the laboratory, specifically the Energy Storage and Conversion Group. Potential candidates are expected
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communication and program management skills are required. This position resides in the Carbon Fiber Technology Facility (CFTF) and the work is performed at the Manufacturing Demonstration Facility (MDF) in
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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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operational and application software; and educates and trains the next generation of computational scientists. A postdoctoral associate position is available immediately for candidates interested in and capable