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user support. Familiarity with scientific software, Linux systems, and parallel computing frameworks. Special Requirements: Visa sponsorship is not available for this position. This position requires
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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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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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manage large-scale HPC storage systems, including parallel file systems such as Lustre, GPFS/Spectrum Scale, BeeGFS and WEKA. Design, implement, and operate large-scale Ceph storage clusters for HPC and
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software engineering practices. Experience with GPU computing (e.g., CUDA, HIP), parallel computing (e.g., MPI, Actor Model). Familiarity with containerization (e.g., Docker, Podman, Apptainer), networking
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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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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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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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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