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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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would co-develop the research objectives and select the methods to be implemented with supervisory support. Some ideas to discuss include integrating repeat GEDI LiDAR surveys with time-series
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such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives
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nature-based solution opposed to traditional ‘grey’ engineering, offer catchment-level solutions by using natural processes to slow and store water through a series of diffused interventions. Historically
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to air pollution in the future, and planning further policy changes. This PhD project will develop statistical modelling frameworks that are able to handle large-scale, complex, and correlated time series
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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to work with international partners such as the WHO. The models developed will used to answer a series of substantial questions related to variation in exposure to air pollution over space and time
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doctoral studentship. This is a fully funded full-time international studentship for three years, commencing January 2026. The prospective doctoral student will focus their attention within the broad field
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doctoral studentship. This is a fully funded full-time international studentship for three years, commencing January 2026. The prospective doctoral student will focus their attention within the broad field