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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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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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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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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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robot learning and edge deployment. Closing date: 15 August Overview Robotics is entering a new phase where foundation models connect perception, language and action. Vision-language-action models, robot
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. Applications are invited for a full-time Research Assistant in Computer Vision and Machine Learning (Visual-AI) with a particular emphasis on deep learning for anomaly and out of distribution detection. The post
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and implement machine learning systems, models, and schemes Research and apply state-of-the-art machine learning prototypes to sponsor specific domains Search and select appropriate data sets before
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multidisciplinary working groups. Provide support to HAIC researchers to use research computing hardware and software for workflows involving AI, data science, machine learning, and physics simulation, especially
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with AI and machine-learning concepts and image-based pipelines (segmentation, classification, structure-from-motion photogrammetry). 6. Git and GitHub version control and modular code development
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, or computer science. Core competencies: solid background in quantum many-body physics strong programming skills (Python required, Rust a plus) experience with tensor networks, variational Monte-Carlo, machine learning