Data Science of the Natural Environment

Accurate predictions of ice sheet change are crucial in planning global sea level adaptation and mitigation measures.

Effective air quality modelling and mitigation is necessary to reduce the 3 million global deaths per year attributable to this cause.

In a changing climate, land use decisions must be made between food, timber, energy, recreation, urban settlement, employment and aesthetic benefits.

This theme will develop a suite of inference and prediction techniques for environmental problems, bringing together existing technology and new developments in a modular framework.

We will enhance model-based statistics with modern machine learning techniques to ensure they scale to modern heterogeneous data sources and end-user requirements.

This theme will focus on the development and deployment of virtual labs as a key instantiation of the integration of heterogeneous data and models that is central to our project.