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

College of Engineering, Mathematics and Physical Sciences

Monday, August 31, 2020 Competition Funded PhD Project (Students Worldwide)

About the Project

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).

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.

For more information about this studentship including how to apply, please follow the instructions detailed on the following webpage

Funding Notes

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

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