250 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"NOVA.id" PhD positions in United Kingdom
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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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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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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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within the climate change domain. The techniques are based on statistical and computational approaches, including machine learning algorithms. The project aims first to contribute to the prevention of fake
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sciences, AI, machine learning or related fields. Strong background and track record in the development of geospatial foundation models from multi-modal Earth Observations is essential as well as strong
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and architectures that support efficient, secure, and scalable machine learning operations (MLOps) across resource-constrained environments for Edge AI. Ethical, and responsible FL for healthcare: In
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Super-Resolution Radial Fluctuations (SRRF) approach. The objective is to build machine-learning models that exploit the underlying physics of fluorescence fluctuations to deliver high-resolution, low
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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