Exeter is very proud of its involvement in the Erasmus+ programme and supports the UUKi Stand Out Campaign to increase outbound mobility from the UK.Exeter saw a 1.34% decrease in student mobility from 2016/17 to 2017/18 however we had planned for this decrease and originally planned to send 483 students but managed to send 515 students on study and work placements. This decrease was largely due to the shadow of Brexit cast upon the Erasmus+ programme and partly due to an increasing trend for students to participate in shorter global opportunities such as summer schools and week-long intensive programmes. We were delighted to host eight Erasmus exchange partners at our Study and Work Abroad fair on Wednesday 31st October 2018 who directly promoted their institutions to our students which we hope will have a positive impact on recruitment to our 2019-20 Erasmus+ mobility. We were delighted to see a modest increase in staff mobility from 3 in 2016/17 to 9 in 2017/18, an area of mobility we are proactively promoting to all staff across the institution with priority going to staff who have not attended an Erasmus+ staff mobility previously.

Our interaction with the natural environment plays a crucial role in all aspects of society: our health, wealth, safety and future development. The increasing availability of large and complex data sets from diverse sources (e.g. environmental monitoring; satellite remote sensing; climate modelling; electronic medical records; social media; and contributions from citizen science) presents an exceptional opportunity to transform our understanding of both the effects of environmental change and our planet-transforming power. This PhD will develop methods for integrating of data from multiple inter-related sources using Artificial Intelligence (AI) to provide evidence for informed decision-making and increase our understanding of environmental challenges. The ability to fully exploit the power of data offers the possibility of a step-change in our ability to respond to some of the most pressing issues facing society; including climate change, health oceans and clean air. Achieving this goal will require the ability to: source and integrate data from multiple sources; develop and apply modelling and computational frameworks that take account of both the data source and the application in question; meet the operational needs of end-users, including accessible computational facilities, suitable time-lags between data retrieval and processing, and the production of user-defined outputs that integrate with existing business processes. The result would be the information and tools that are required for decision making across a variety of sectors, including energy, water, transport, agricultural and government policy. It will also support efforts to address some of the most important challenges faced by society today, including mitigation and adaptation to climate change, air quality, reduction and reparation of environmental and ecosystem degradation, and preparedness and response to natural hazards. However, there are challenges associated with discovering and accessing suitable data; gaps and inconsistencies in the data and when it is available; and technical complexity in integrating different data types. Similarly, there are often disconnects between the communities generating data, and those who are making decisions. These challenges mean that society has yet to fully exploit the full potential of environmental data to empower individuals, organisations and businesses and support informed decision making. This PhD will aim to address these challenges and, ultimately, to provide a step-change in our understanding of the effects of changes in our environment and to provide solutions to challenges based on the use of data and AI.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.