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Engineering at Scale : Profile, model, and optimize distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross
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Engineering at Scale: Profile, model, and optimize distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross
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limited to Slurm, MPI, CUDA, and Harbor depending on program needs. Basic Qualifications: BS degree in computer science, computer engineering, information systems or a related field, and 5 years of
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, OpenMP and CUDA. Knowledge of research software engineering principles and practice. Experience with modelling and simulation of complex, interdisciplinary systems. Knowledge of computer
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similar). Experience with parallel programming frameworks (e.g., MPI, OpenMP, CUDA) and high-performance interconnects (e.g., InfiniBand). Preferred Qualifications: Familiarity with advanced storage
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., Ganglia, Grafana, or similar). Experience with parallel programming frameworks (e.g., MPI, OpenMP, CUDA) and high-performance interconnects (e.g., InfiniBand). Preferred Qualifications: Familiarity
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-squares solvers, or scalable tensor kernels. GPU performance engineering — CUDA/HIP kernel design, communication–computation trade-offs, or performance modeling on heterogeneous systems. Theory of
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. Experience with performance tuning and benchmarking tools for HPC environments (e.g., Ganglia, Grafana, or similar). Experience with parallel programming frameworks (e.g., MPI, OpenMP, CUDA) and high
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the following: MPI OpenMP CUDA HIP Kokkos Raja Preferred Qualifications: Experience building and installing scientific software and other 3rd party software applications on HPC systems. Experience
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such as SLURM, PBS, or similar platforms. Experience with parallel computing technologies such as MPI, OpenMP, and/or CUDA and high-performance interconnects such as InfiniBand. Experience with HPC