Students who successfully pass the MRes will continue to PhD, undertaking a 3-year specialist training programme to apply AI methodologies. The Centre will address problems that are relevant to building resilience to environmental hazards and managing environmental change.

PhD projects will be focused on two key research themes. These themes will also touch on widely-applicable emerging methodologies (e.g. provenance, data and model curation), and will serve as context in which different modelling methodologies can be compared. 

[1] Environmental data classification, integration & analysis - using machine learning to process data to provide actionable information. AI will underpin next generation data-analytics systems that can, e.g., process data from diverse sources (including sensors on the ground, in the air or in space) and classify them into categories that humans can understand. The data can be optimally combined to generate, e.g., key indices to track progress against sustainable development goals or information to ensure conservation efforts and resources are deployed efficiently and cost-effectively.

[2] Environmental modelling - developing new computer models for environmental problems using data-based approaches. Smart post-processing of model output using data-driven approaches can generate bespoke results, e.g., bias-correction, downscaling and optimal weighting of ensemble climate model output to generate high-resolution decision-relevant information. AI approaches can also be used to form reduced models or to develop new empirical parameterisations for models.