PhD Studentship: Towards Responsible AI Systems for Automated Biodiversity Monitoring

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 click here

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 mid-day Friday June 28^th 2024 (BST), with interview to be w/c 29^th July 2024 (tbc).

The start date is expected to be Monday January 6^th 2025.

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

THEME - Digital Worlds & Disruptive Technologies

Project Description

To address global biodiversity loss, we need to understand how biodiversity is changing. However, current levels of biodiversity change far exceed our monitoring capabilities. Traditional field surveys alone cannot fill the gap, and so scalable, passive monitoring technologies, like camera traps, bioacoustics, and eDNA, have emerged as solutions. Camera traps are the most widely used of these technologies. However, for camera trap data to be useful, any species in each image or video must be identified. Manual identification is slow and, thus, AI tools to automate the process have proliferated. These species identification AIs are increasingly being used by conservationists, as well as banks and businesses looking to comply with biodiversity net gain, profit from biodiversity credits, or transition to being nature positive. AI can produce revolutionary benefits, but it can also create harms if not developed responsibly. Species identification AI is in its infancy: the first seminal study was published five years ago; current models generalise poorly and can be highly inaccurate; and we have no understanding of how accurate these systems need to be, or the consequences if they are not. That AI is being deployed today, in the presence of these knowledge gaps, is concerning.

Advert information