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Field
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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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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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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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-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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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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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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network analysis of interactions between these features. These pipelines and computational tools will be used to integrate imaging and genetic data, and maps of myelin distribution in the cortical grey
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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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Location: South Kensington About the role: The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple
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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