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Field
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-of-the-art AI computing infrastructure with large-scale GPU clusters. * Opportunities to collaborate with investigators across Mayo Clinic Arizona, Rochester, and Florida campuses. * Integration with
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working on the LHCb experiment at the Large Hadron Collider. The successful candidate will focus on developing LHCb’s real-time data analysis systems, including the GPU-based software trigger. This work
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microservices, large language model (LLM) inference servers on GPU clusters, vector database and retrieval-augmented generation (RAG) pipelines, and observability stacks that advance AI capabilities across
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, virtualisation technologies and performance optimisation. Hardware acceleration technologies and modern compute architectures, including GPUs and high-performance networking technologies. Programming and scripting
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. Applicants are expected to have strong programming skills in Python, hands-on experience with PyTorch, and practical experience with GPU computing. Experience with engineering simulation, computational
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, deployment, and test activities • Experience integrating software with hardware, sensors, or embedded systems • Familiarity with GPU-based code development (e.g., CUDA, TensorFlow, etc.) • Familiarity with AI
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. Experience with heterogeneous and parallel computing technologies, programming models, or accelerators, including CPUs, GPUs, FPGAs, and emerging computing technologies. Familiarity with quantum software
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RFSoC platforms, and CUDA coding for GPU acceleration.
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their understanding and utilization of HPC resources. KNOWLEDGE Required Knowledge of a variety of HPC systems (CPU, GPU, storage systems, file systems, networking, virtualization, job schedulers (Slurm) and scientific
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), which operates a large multi-thousand-core cluster with GPU co-processors and PhD-level consulting staff. Pitt ECE also leads the NSF Center for Space, High-performance, and Resilient Computing (SHREC