University of Exeter funding: QUEX PhD Studentship

Using deep learning to improve emergency response in natural hazard management. PhD Computer Science. PhD Studentship (Funded by the QUEX Institute) Ref: 3896

About the award


Lead Supervisor: Professor Guangtao Fu, Professor of Water Intelligence, University of Exeter

Second Supervisor: Dr Alina Bialkowski, Lecturer in Computer Science, University of Queensland

Join a world-leading, cross-continental research team

The University of Exeter and the University of Queensland are seeking exceptional students to join a world-leading, cross-continental research team tackling major challenges facing the world’s population in global sustainability and wellbeing as part of the QUEX Institute. The joint PhD programme provides a fantastic opportunity for the most talented doctoral students to work closely with world-class research groups and benefit from the combined expertise and facilities offered at the two institutions, with a lead supervisor within each university. This prestigious programme provides full tuition fees, stipend, travel funds and research training support grants to the successful applicants.  The studentship provides funding for up to 42 months (3.5 years).

Eight generous, fully-funded studentships are available for the best applicants, four offered by the University of Exeter and four by the University of Queensland. This select group will spend at least one year at each University and will graduate with a joint degree from the University of Exeter and the University of Queensland.

Find out more about the PhD studentships

Successful applicants will have a strong academic background and track record to undertake research projects based in one of the three themes of:  Healthy Living, Global Environmental Futures and Digital Worlds and Disruptive Technologies.

The closing date for applications is midnight on 31 August 2020 (BST), with interviews taking place week commencing 12 October 2020.  The start date is expected to be April 2021.

Please note that of the eight Exeter led projects advertised, we expect that up to four studentships will be awarded to Exeter based students.

Project Description

Natural disasters are among the world’s greatest challenges and 80,000 people per day are affected with an economic loss of US$ 1.5 trillion since 2003. Flooding alone, which is the most frequent and wide-reaching weather-related natural hazards in the world, has affected 2.3 billion people with an estimated economic losses of US$ 662 billion from 1995 to 2015, and US$ 60 billion in 2016 alone. In both UK and Australia, the impacts of floods and droughts are projected to increase in the future due to climate change, population increase, and aging water infrastructure and lack of investments. 

This project aims to develop a deep learning approach and tool for rapid, large scale assessments of the impacts of natural hazards with an aim to optimizing emergency planning and operation. Its main objectives are to 1) build open source datasets of natural hazards from multi-sources such as satellite imagery, CCTV images and social media, 2) develop a deep learning algorithm based on convolutional neural networks (CNNs) to detect impact extent and vulnerable objects such as human or cars, 3) use reinforcement learning to develop effective emergency responses considering system interdependencies and people behaviours, 4) analyse the impact of people behaviours on emergency planning. This project will focus on the following three natural hazards: floods, droughts and bushfires. The outcome of this research will be fed directly into emergency planning and response in order to reduce the risks of natural hazards, and will be tested in real-world scenarios through collaborations with our industrial partners. For example, in the recent event of Storm Dennis, a woman was trapped on the roof of her submerged car for 12 hours before being rescued in England. This tool will be able to identify such situations using remote sensing or CCTV imagery.   

This project will be supervised by a strong team with significant research strengths in natural hazards and artificial intelligence (AI) research, based at the University of Exeter and the University of Queensland. The student has a unique opportunity to access the resources and facilities at the national institute for data science and AI – Alan Turing Institute. 

The timeliness and originality of this project lie in the following aspects: 1) building on the latest breakthroughs in deep learning, this project will develop new AI algorithms to tackle a pressing global challenge, i.e., to reduce the damages of natural hazards; 2) the topic of this project is well aligned to the research priorities of the Institute of Data Science and Artificial Intelligence, the QUEX Institute and the Alan Turing Institute

Entry requirements

Applicants should be highly motivated and have, or expect to obtain, either a first or upper-second class BA or BSc (or equivalent) in a relevant discipline.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests.

How to apply

You will be asked to submit some personal details and upload a full CV, supporting statement, academic transcripts and two academic references. Your supporting statement should outline your academic interests, prior research experience and reasons for wishing to undertake this project, with particular reference to the collaborative nature of the partnership with the University of Queensland, and how this will enhance your training and research.

Applicants who are chosen for interview will be notified week commencing 5 October 2020, and must be available for interview week commencing 12 October 2020.

Please quote reference 3896 on your application and in any correspondence about this studentship.


Application deadline:31st August 2020
Value:Full tuition fees, stipend of £15,000 p.a, travel funds of up to £15,000, and RTSG of £15,000 are available over the 3.5 year studentship
Duration of award:per year
Contact: PGR Admissions Office