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Field
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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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scanning confocal microscopy and calcium imaging in time-lapse, computational imaging approaches for analysis of images and movie recordings, analysis of the connectome to identify neural circuits
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VR/AR, quantum tech, life-sciences, computing and biomedical imaging. The project will work on cutting-edge optical technologies alongside collaborators Prof Melissa Mather, Prof Dmitri Veprintsev, and
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This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture
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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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of the underlying the physical processes leading to predictability and forecast skill of North Atlantic cyclones. The project will also explore statistical post-processing methods to see if raw forecast skill can be
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Applications are invited from PhD studentship candidates with good first degrees in computer science, physics, maths, biology, neuroscience, engineering or other relevant disciplines to join
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combination of experimental work, rig development, and computational modelling, and will equip the student with a unique skill set highly relevant to both academic research and industrial careers such as
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decision processes. Use the CARLA simulation platform to generate DCD-style data in high-risk or ambiguous driving scenarios. Build a proof-of-concept verification pipeline that maps DCD outputs
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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