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Field
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the central challenge hindering this vision: the fundamental incompatibility between text-native LLMs and the operational reality of computer networks. Directly applying LLMs is impeded by three core technical
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geometry altogether and operate in hyperbolic space. Our lab has published multiple papers showing that hyperbolic deep learning has strong potential for computer vision, from hyperbolic image segmentation
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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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Knowledge of medical imaging Good knowledge of image processing Good knowledge of signal processing Good knowledge of AI for medical imaging Ability to use computer programming tools Ability to use word
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the project is the development of AI-based pipelines for detecting, segmenting, and classifying lichen communities. Convolutional neural networks (e.g., U-Net, DeepLab) and machine-learning algorithms (e.g
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biological materials. The development of novel computer-vision-based techniques for contactless detection, quantification, and prevention of sport injury. The development of robotic humanoid simulator and
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guidance and robotics. Our work combines medical imaging, computer vision, and machine learning with strong clinical translation, in close collaboration with Balgrist University Hospital and the national
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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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& Machine Learning • Clinical pathways and decision support for patients with acute chest pain • AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis • Speaking
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and