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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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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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unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
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, combining both accuracy and explainability; (3) extend statistical learning theory to offer theoretical bounds for intrinsically-aligned AI models; (4) employ the newly-developed metrics to train deep neural
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Deep brain stimulation (DBS) is a medical therapy for neurological disorders, in which an implanted system provides electrical impulses to dysfunctional brain areas to alleviate patients’ symptoms
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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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analysis will focus on building sophisticated Deep Learning models, e.g., Long Short-Term Memory (LSTM) networks, to accurately model DPs over time and predict mood deterioration. The project will implement
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of Sentinel-2 fluvial scenes’. Earth Surf. Process. Landforms, 45, 3120–3140. Carbonneau et al 2020) ‘Adopting deep learning methods for airborne RGB fluvial scene classification’. Remote Sensing of Environment
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
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-supervised learning, and few/zero-shot techniques — the student will adapt models to ecological data. Bayesian deep learning and ensemble methods will be explored for trustworthy uncertainty estimation