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problems in health data science. Air pollution is composed of several different environmental pollutants, for example particulate matter (PM10 and PM2.5), ozone (O3), nitrogen dioxide (NO2) and sulphur
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The rapid growth of the data economy and the privacy implications accompanying it have motivated a new paradigm shift towards decentralisation of data on the Web, which aims to foster data
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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Organization (WHO) estimates that 4.2 million premature deaths every year can be attributed to fine particulate ambient air pollution (PM2.5) [1]. Comprehensive information is required on air quality to provide
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-developing the principles (e.g. ethics) and methods for anonymising, processing and analysing sensitive data collected by a national charity’s 24/7 helpline for people experiencing or witnessing elder abuse
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-critical decisions in real time. These systems rely heavily on sensor data (e.g., GPS, pressure transducers, image processors), making them vulnerable to stealthy threats like False Data Injection (FDI) and
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November 2025 or as soon as possible thereafter. This PhD project aims to explore how emerging datasets could provide value to the UK’s insurance industry through a combination of data analytics, modelling
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, acoustics, and AI. AURORA³ will enable fast, accurate and reproducible data collection thanks to a state-of-the-art acoustic anechoic chamber equipped with a spherical loudspeaker array and a world-first
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-service involvement, develop typologies of need, and uncover spatial and temporal trends. Combining data science techniques with stakeholder engagement, the project aims to generate actionable insights
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engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early