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, cybersecurity, software and hardware accelerators such Data Plane Development Kit (DPDK), eBPF, SmartNICs, P4 programmable switches, and GPUs. Situated in USC’s Engineering and Technology Innovation Center
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cluster consisting of 16,616 CPU cores and approximately 100 GPUs, as well as a dedicated AI cluster with 40 A100/H100 GPUs. Position Overview The Department Chair will serve as the academic and
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 3 months ago
for MPI, GPU architectures, and possibly non-x86 CPU architectures); groundwork on Guix, such as: supporting non-root usage; ensuring adequate continous integration and automation tooling; organizing and
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methodology Assist with code optimization and integration into Department of Energy (DOE's) applications running on the exascale computer systems with GPU accelerators We are looking for: PhD or equivalent
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maintenance. Proficiency in programming languages such as Python, R, C#, and SQL. Familiarity with high-performance computing, cloud computing (e.g., AWS), GPUs, and DevOps tools, including version control
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-access AI resources. Experience in GPU-accelerated computing and reproducible software development, including the use of containerization frameworks (e.g., Docker, Singularity) and collaborative code
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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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that serves researchers and educators at the University of Utah and beyond. Responsibilities Kubernetes for AI/ML: Design and deploy highly available Kubernetes clusters, optimized for GPU utilization and AI/ML
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to push the boundaries of what’s possible. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. We don’t
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plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov solvers