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Methods In this project we want to provide a better understanding of how cloudiness affects, and is affected by, environmental factors called “cloud controlling factors”. Success of this goal will be
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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to quickly quantify the damage to forest plantations after a cyclone or a tropical storm. There is unrealised potential in using multi-modal computer vision methods that synthesis multi-source Earth
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Methods The Arctic is warming at over three times the global average, yet the underlying drivers of this amplification remain poorly constrained. Groundbreaking results from the recent MOSAiC expedition
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Methods Volcanic eruptions threaten 10% of the world’s population. To enable eruption forecasting, carry out hazard assessment, and mitigate risk, a thorough understanding of volcanic unrest is essential
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addition, data augmentation methods to improve generalisation will be explored, for example through structured transformations such as permuting satellite IDs to enforce homogeneity. The project may also
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Methods River migration (by bank erosion and avulsion) displaces thousands of vulnerable people annually, is likely to worsen significantly under climate change, and is challenging to predict. This project
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Methods Mountain rivers are an essential source of water, nutrients and hydropower, yet these resources are threatened by increasing quantities of sediment and pollution along water courses, partly linked
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Methods This project will explore how Interferometric Synthetic Aperture Radar (InSAR) loop closure, derived from the summation of phases in three closed multilooked interferograms, can be used to monitor
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Methods Selfish X chromosomes are a fascinating form of ‘unfair’ mendelian genetics. Males bearing a selfish X can sire only female offspring as Y-bearing sperm are destroyed during spermatogenesis by