256 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL" PhD positions in United Kingdom
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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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INTENSIVE LEARNING ACADEMY FOR INNOVATION IN HEALTH AND SOCIAL CARE The All-Wales Intensive Learning Academy for Innovation in Health and Social Care has been developed to deliver a world-class learning
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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and Technology (CST) at the University of Cambridge. The goal of this PhD programme is to launch one "deceptive by design" project that combines the perspectives of human-computer interaction (HCI) and
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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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refinement or a loss of fidelity in critical regions. Machine learning provides a promising route to capture these relationships more systematically by identifying how local geometric features determine the
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. Current XAI methods are often generic and overlook industrial realities. This project will embed user-centric explanations directly into machine learning workflows using structured, ontology-driven
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, programming, signal analysis or machine learning are particularly valuable. If you are keen to apply technology to improve global healthcare, we would be delighted to hear from you. Entry requirements
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Wales, meaning most paediatric records are handwritten and unstructured. The project will prioritise digitising these records using natural language processing (NLP) and machine learning (ML) to create
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on combining innovative technologies such as remote monitoring, large language models, machine learning, blockchain, and eco-accounting to enhance the efficiency, security, and sustainability of e-bike charging