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for downstream tasks. In this project, you will develop novel unsupervised machine learning methods to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a
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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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an exceptional opportunity to conduct cutting-edge research at the intersection of machine learning, healthcare, and computational modelling, contributing to real-world clinical impact. What is offered
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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
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an exceptional opportunity to conduct cutting-edge research at the intersection of machine learning, healthcare, and computational modelling, contributing to real-world clinical impact. What is offered
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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successful courses or projects) Be proficient in programming (preferably in Python ot Matlab). Ideally familiar with machine/deep learning, signal processing, dynamical system or mathematical modelling To find
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. when do we stop modelling? How do we track / score the quality of the model? What is the required level of quality over time? How can quality be brought to the required level? Can Machine Learning, Large