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Field
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process. Address blind inverse problems by defining a network to learn distortion functions from data, informing the optimization in the learning process. Refine optimization and learning strategies
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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driven research Maths competency and experience with statistics in research Software competency: ImageJ, Matlab, Graphpad Prism, R Evidence of Github use An interest in cardiovascular physiology
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the Department of Biomedical Engineering at Swansea University, but you will also interact closely with our national network of clinicians from across the UK. This ensures the project stays grounded in clinical
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to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered electronics research. This project will be conducted within Cranfield’s Integrated Vehicle Health
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disorders network and through peer reviewed publications. PhD description: Based at the IMH, Nottingham, this PhD will be a jointly supervised mixed-methods project, initially assessing what service key
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glaucoma (POAG) is the leading cause of irreversible blindness worldwide. It arises from fibrotic remodelling of the trabecular meshwork, a filter-like structure in the anterior chamber of the eye. As the
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labs framework. This studentship will include cohort-based training and activities, enabling students to gain wider skills and develop valuable personal and professional networks. Project Description