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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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bottleneck in the screening process. This PhD project will address this through deep integration of scanning probe electrochemistry, optical microscopy and machine vision, to develop a system that can
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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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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of fixed bottom deep water wind energy. The role is for the second intake of the EnerHy programme which commences in October 2025. Research project overview: While floating supports remain
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, aligning with the Medicine without Doctors wider vision and combining methodological approaches from medical sociology, medical anthropology, STS, and history, to study the emergence and standardisation
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available