117 parallel-phd "https:" "UNIVERSITY OF THE SUNSHINE COAST UNISC" positions at Oak Ridge National Laboratory
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be found at https://datascience.cancer.gov/collaborations/nci-department-energy-collaborations ; https://www.cincinnatichildrens.org/research/divisions/d/decoding-mental-health/programs/mental-health
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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collaboration. Present and report research results and publish scientific results in peer-reviewed journals or conferences. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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respectful workplace – in how we treat one another, work together, and measure success. Required Qualifications: PhD in Nuclear engineering, computer science, applied mathematics, or a related field completed
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-flows. Basic Qualifications: PhD in mathematics, computer science, engineering, or related field earned within the last 5 years. Preferred Qualifications: Experience with mesh generation/CFD applications
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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: Candidates must have a MS degree or PhD in Nuclear engineering, computer
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considered. Masters and PhD degree holders in the same fields of study are also encouraged to apply: Masters’ holders should have a minimum of seven (7) to ten (10) years of relevant and aligned experience
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, and measure success. Basic Qualifications: A PhD in materials science and engineering, mechanical engineering, aerospace engineering, polymer science, or a related discipline completed within the last
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, enhanced sampling, QM/MM) Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation