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Volcanic islands are exciting real-world climate and landscape laboratories. When geologically young (<10Ma) they provide a simplified and spatially limited setting for testing landscape evolution
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datasets with phylogenies and environmental variables, the project aims to rapidly explore trait evolution, predict dispersal potential, and assess climate-related risks. This work bridges biodiversity
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, there is a critical need to develop and implement life cycle carbon accounting tools and optimization techniques tailored to the specific operations and materials used in the pipe industry. This PhD is co
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Science), a global leader in marine science, the project will develop scalable, low-cost embedded vision systems to analyze marine biodiversity and detect anthropogenic debris. The core challenge is
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. road, power distribution). This project will develop new insights into what drives flooding and extreme wind to co-occur on timescales from (sub-)daily to seasonal. Our recent work suggests that
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. Development of novel processing techniques Modelling techniques that can inform the direction of experimental activity Physical, mechanical and materials characterisation techniques Data-driven approaches
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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis
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under multiple environmental and socio-economic scenarios. You’ll develop sought-after skills in geospatial analysis, hydrodynamics, sediment transport, machine learning-assisted detection, and hydro
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This project aims to bring together evidence from climate projections, risk assessment and observations to develop and evaluate event-based storylines based on recent flooding in Leicestershire, UK
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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven