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assess how effectively imaging techniques capture or enhance the taxonomic resolution of traditional net sampling. Training You will gain skills in plankton taxonomy, image processing, ecological
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new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will benefit from the extensive and broad
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in AI. Previous publication record in relevant fields: AI, machine learning, computer vision, etc. Previous successful project on a relevant topic. Good knowledge of statistics, probability
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: environmental monitoring, AI, computer vision or multispectral imaging. Entry Requirements At least UK equivalence Bachelors (Honours) 2:1. English Language requirement (Faculty of Science equivalent: IELTS 6.5
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for Real-World Optimisation and AI Applications Brain-Computer Interfaces & their Applications Computational Neuroscience: Reinforcement Learning and Microzones in the Cerebellum Explainable Generative
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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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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront