PhD in Application of Artificial Intelligence in the Monitoring of Zoo Animal Welfare

In the face of rapid biodiversity decline, zoos play a key role in wildlife conservation by engaging in specialised captive breeding programmes that maintain genetically diverse and sustainable animal populations that could be reintroduced in the wild. Additionally, research in zoos provides new insights into animal biology and behaviour that can help the management of wild populations. In this context, one of the priorities in zoo management is the promotion of high animal welfare through the monitoring of both their physical and mental health. To date, however, the assessment of captive animal welfare has largely relied on direct observations and physiological data collection. These traditional methods are time consuming, require a substantial number of resources and can often be performed only by trained specialists. For this reason, it is pivotal to design more efficient and cost-effective methodologies to monitor captive animal welfare.

The aim of the proposed interdisciplinary PhD project is to combine state-of-the-art artificial intelligence (AI) and machine learning (ML) to perform captive animal behaviour analysis from live image stream and integrate this information with physiological data to create individual animals’ physical and mental health profile over time and detect any abnormal behavioural and physiological parameter that can indicate issues with animals’ physical and mental health. The project will be conducted on the captive colony of chimpanzees housed at Twycross zoo and will involve training the machine to: (a) individually identify the chimpanzees, (b) recognize animals’ behaviour (e.g., food intake, resting), (c) assess animals level of stress and (d) integrate this information with physiological data (e.g., heart rate, body score). The ultimate goal is to develop an innovative technology that can help keepers and researchers to constantly and automatically monitor captive animals’ welfare and immediately detect any issue that may require immediate intervention.

The ideal candidate should have a background in computer science or bioengineering with interest in the field of animal behaviour and welfare. They should be reliable, patient, enthusiastic, committed to scientific research, and able to work both as part of a team and individually.

Salary: Fully funded for UK / EU / International students - 3 years of stipend at UKRI rates

Closing date: 12/01/2024

Further information can be found here.