Deep space-time models for modelling complex environmental phenomena

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This project aims to adapt deep-learning models, used in areas of artificial intelligence such as image tagging and automatic text translation, to improve our understanding of the environment. The project expects to develop new theory for deep-learning models to learn from measurement data and numerical-model output about environmental phenomena that evolve in space and time, such as ice sheets and the atmosphere. Expected outcomes include the ability to provide reliable predictions and quantification of uncertainty on environmental concerns of national importance, such as sea-level rise. Key benefits include improved risk management and mitigation, for example through financial incentives or infrastructure planning.