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London School of Economics and Political Science; | London, England | United Kingdom | about 2 months ago
of research computing at LSE. Your expertise will be key in future-proofing our research hardware environment, ensuring high availability, scalability and security across HPC clusters; GPU acceleration, high
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computer hardware and network infrastructure for the ITS Research services. This covers the 12000 core, 100 GPU QM High Performance Compute cluster (see https://docs.hpc.qmul.ac.uk ), various hosted
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projects to the Tier 2 supercomputer Bede (32 IBM Power 9 dual-CPU nodes, each with 4 NVIDIA V100 GPUs and high performance interconnect). The is also well-provisioned with computational resources and you
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AI pipelines (similar to the attached prototype). Enable users to configure model parameters, connect modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU
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-meter optical/IR survey telescope, the LIGO laboratory, and a dedicated CPU+GPU computing cluster at the Massachusetts Green High Performance Computing Center. The appointment will be for a maximum of
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communicate results clearly in writing and presentations. Desired Qualifications: Knowledge of GPU architecture and GPU programming. Interest or experience in distributed training on large scientific datasets
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projects to the Tier 2 supercomputer Bede (32 IBM Power 9 dual-CPU nodes, each with 4 NVIDIA V100 GPUs and high performance interconnect). The is also well-provisioned with computational resources and you
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the next generation of AI leaders, mentor students on groundbreaking research projects with access to state-of-the-art GPU hardware, forge partnerships with industry and academia, and contribute to AI
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, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding
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) for reproducible research workflows. Support Optimising GPU-accelerated workloads (e.g., PyTorch, TensorFlow), including multi-GPU scaling and distributed training. Develop training materials, documentation, and