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for all. This PhD project aims to develop, physics-informed surrogate models to support the design and optimisation of deep geothermal energy systems under subsurface uncertainty. Focusing initially
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EPSRC ReNU+ CDT PhD Studentship: Physics-informed machine learning for deep geothermal systems under uncertainty. Award Summary 100% fees covered, and a minimum tax-free annual living allowance
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The University of Exeter has a number of fully funded EPSRC (Engineering and Physical Sciences Research Council ) Doctoral Landscape Award (EPSRC DLA) studentships for 2026/27 entry. Students will be given sector-leading training and development with outstanding facilities and resources. The...
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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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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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aims to characterise the sequence, structural, and functional properties of UL-CDRs using deep learning and structural bioinformatics, with the goal of identifying novel antimicrobial peptide candidates
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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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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Bachelors Honours degree (or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject Area Medical imaging, biomedical