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Field
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machine learning, computer vision, robotics, efficient inference or embodied AI, prioritising practical, reliable systems. Durham offers strong facilities: Bede HPC (128 GPU), GPU cluster (90+ GPU), LiDAR
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computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications for atmospheric
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, AI agent orchestration platforms and GPU-enabled AI infrastructure. Responsibilities: Platform operations and reliability Own day-to-day operations of SEA-LION API Farm, our multi-cloud LLM inference
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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hardware (e.g., GPUs and/or Non-volatile memory) and data science applications.
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systems and underwater imaging. 4. Linux system administration and scientific computing workflows, including environment management, GPU-accelerated workloads, and remote/headless operation. 5. Familiarity
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, or GPU programming is an asset LanguagesFRENCHLevelGood LanguagesENGLISHLevelGood Research FieldEngineering » Electronic engineering Additional Information Selection process Applications must be sent
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functioning in a dynamic technological environment. Preferred Qualifications: Experience with parallel computing, GPU operation (CUDA Toolkit), multi-GPU training, and distributed frameworks for machine
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, GPU) and workshops for mechanical, electrical and electronic development projects. Long-standing and very successful cooperations with industry and clinical partners (cardiology, radiology) offer
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-area surveillance video feeds. In addition Durham University hosts the UK regional supercomputer Bede (128 NVIDIA V100 + 3 NVIDIA Hopper GPUs) in addition to the pan-university Hamilton GPU resource (8