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management 5. Familiarity with cloud or on-prem compute environments 6. Experience with distributed computing or GPU acceleration Required Knowledge, Skills, and/or Abilities 1. Ability to obtain a U.S
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object storage for the concurrency, small-file, and large-checkpoint patterns of distributed GPU training and HPC simulation. Operate at scale, safely. Design and run multi-petabyte storage with
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and GPU servers for the delivery of the PC exercises, and jointly supervising the PC exercises during the course What you contribute Student on a STEM degree programme Good knowledge of at least one of
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-dimensional biological datasets. Familiarity with GPU computing and high-performance computing (HPC) environments. Other Requirements Ability to work collaboratively with researchers across computational and
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-Wyoming Supercomputing Center (NWSC), GPU computing resources, and dedicated funding to support research growth. In addition, candidates with energy-related expertise may qualify for funding support from
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Ability to balance engineering rigor with the pace of a fast-moving research org Nice to have: Experience with CUDA kernel development or GPU optimization Familiarity with LLM training pipelines Experience
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HiPerGator 3.0 , one of the most powerful supercomputers available to a University in the world. It includes 49,920 AMD cores for traditional computing and 608 NVIDIA GPUs for artificial intelligence analyses
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. Implement scalable training and inference pipelines using modern ML tooling (e.g., PyTorch/TensorFlow/JAX), version control, containers, and HPC/GPU resources. Support the publication of intermediate data
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-efficient designs, GPUs and HPC), Data Science/AI/Machine Learning (e.g., fundamentals, trust and explainability, LLMs, autonomous systems, computer vision), Security (e.g., fundamentals, hardware/software
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programming LAMP stack design and implementation experience Knowledge of GPU and FPGA cluster management Experience with federal research compliance and security requirements Background in AI/ML computing