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the rich, unstructured information found in clinical notes and cannot effectively gather data on lifestyle and social determinants of health. This PhD project will pioneer a novel, hybrid AI framework
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projects in the Centre for AI and Robotics Research. Funded PhD projects Adaptive Systems Research Group Artificial Intelligence in Games Continual and Open-ended Reinforcement Learning Information and the
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of bacteria in the human gut, in-depth work with next-generation as well as 3rd generation sequencing technologies, then this PhD will be right for you. The ideal candidate will enjoy learning about metagenomic
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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience studying bumblebee behaviour
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health, and bioinformatics. You will apply advanced AI methods - from classical machine learning to large language models and agent-based AI - on large-scale healthcare datasets, including structured
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measurement; Measurement of related tracers (e.g., Radon); Programming (e.g., R, Python) for advanced atmospheric time-series analyses, including machine learning; Skills for presenting research at scientific
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, fairness). Provenance and integrity of machine learning pipelines. Generative content authenticity. Cyber-physical machine learning systems. Scalability of properties from small to large models. In
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of insect pollinators from large-scale photographic and video datasets. The research will integrate ecological fieldwork, computer vision and stakeholder engagement to: 1.Develop and optimise deep learning