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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme! How does it work? Candidates will study for a four year, full time funded PhD
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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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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
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of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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filled. This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end. You will unify modern machine learning, statistical signal
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opportunity to work across both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student