56 parallel-programming "https:" "IMOST UMR 1240 INSERM" Postdoctoral research jobs at Oak Ridge National Laboratory
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, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts. We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://jobs.ornl.gov
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Demonstrated research experience with HPC, AI/ML and/or distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and
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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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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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learning algorithms for engineering systems Programming experience in FORTRAN, C, or C++ and scripting experience in Python or similar languages Experience with parallel computing environments and Linux
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Theoretical Physics or a related discipline completed within the last 5 years. Experience with High Performance Computing and programming for massively parallel computers. Experience with quantum many-body
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visualization technologies, programming systems and environments, and system science and engineering. Major Duties/Responsibilities: The position requires collaboration within a multi-disciplinary research
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Director's office can be found here: https://www.ornl.gov/content/research-integrity . Basic Qualifications: A PhD in physics, chemistry, biochemistry or a related field completed within the last five years
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with environment, safety, health and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics. Deliver ORNL’s mission by aligning behaviors
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. Scalability of Preprocessing Pipelines: Design and implement automated, parallel preprocessing workflows capable of handling multi-petabyte datasets efficiently while reducing throughput bottlenecks. Data