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Field
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developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation of results. Deliver ORNL’s mission by aligning
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techniques for the generation and exploration of complex, large-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable
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other domain experts to facilitate collaboration. Present and report research results and publish scientific results in peer-reviewed journals or conferences. Basic Qualifications: A PhD in Computer
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-driven experiment design. Optimize pipelines for performance, parallelization, and near real-time operation during beam time. Contribute to simulation tools to test imaging concepts, predict performance
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mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Basic Qualifications: PhD in a computational discipline such as Data
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activities. Your Profile: Master and PhD degree in astronomy, physics, computer science or equivalent fields of study. Proven experience in N-body simulations and the comparison of simulation and observation
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. Applications from candidates not in possession of a PhD will be rejected without further consideration. Candidates who prepared their thesis at the IAC have to present accreditations of complementary training
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platform enables us to test hundreds of different conditions in parallel and assess their impacts on human immune responses, such as antibody production. We routinely work with industry partners to exploit
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calculations (such as with PHREEQC or a similar code). We are running several projects in parallel, ranging from fundamental investigations of mineral-water-gas properties, through developing predictive models
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration