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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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algorithm development and application. Familiarity with common scientific programming languages such as C++. Experience in parallel programming with one or more common parallel programming models, like MPI
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learning algorithms in PyTorch. Expertise in object-oriented programming, and scripting languages. Parallel algorithm and software development using the message-passing interface (MPI), particularly as
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for massively parallel computers. Experience with quantum many-body methods. Preferred Qualifications: A strong computational science background. Familiarity with coupled-cluster method. Understanding
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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
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distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and parallel computing. Demonstrated hands-on experience and
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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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well as experience with HPC environments and parallel computing. Demonstrated hands-on experience and understanding of developing scientific data management, workflows and resource management problems. Strong problem
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-driven 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
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developing or applying parallel algorithms and scalable workflows for HPC resources. Experience developing or applying privacy-enhancing technologies such as federated learning, differential privacy, and