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Field
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., Kubernetes), public cloud hybrid infrastructure, and administering AI/ML hardware (e.g., GPUs) is highly desirable. Demonstrated ability to program in administrative scripting languages (e.g., Python, shell
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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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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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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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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modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU utilization, throughput, ROI impact) in real time. System Integration Work with the research team to connect AI
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facilities. Core responsibilities include the deployment and maintenance of small-scale HPC and compute nodes, GPU workstations, Linux and Windows servers, research data storage and backup solutions
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices