This 4-year PhD studentship is open to both Home (UK) and international applicants. The successful candidate will receive an annual stipend at the UKRI minimum rate (currently £21,805 per year) plus £1,000/annum enhancement, and a research training support grant of up to £20,000 for the full 4-year programme. This project is in competition for funding with other projects. Usually, the projects that receives the best applicants will be successful.
This project is expected to start in September 2027.
The Method for Observing pHysical Activity and Wellbeing (MOHAWk) is a validated systematic observation tool for assessing physical activity, social interaction and other health-related behaviours in public spaces. Although MOWAWk can be applied reliable to video footage, it currently depends on manual activity, with in labour-intensive and difficult to implement at scale. Advances in computer vision, including multimodal large language models and pre-trained video foundation models have the potential to automate this process.
This interdisciplinary PhD will develop and validate an automated version of MOHAWk. The objectives are to:
- Develop an AI-enabled system for identifying MOHAWk behaviours in video footage;
- Validate its outputs against expert manual coding;
- Assess its robustness, generalisability and potential biases across different urban and observational contexts;
- Demonstrate its value through the evaluation of a real-world environmental intervention.
Potential outcomes:
- An open-source framework and software for scalable behavioural observation;
- Evidence about the accuracy, robustness and limitations of foundation-model approaches to analysing behaviour in public spaces
- A platform for future applications across public health, urban planning, transport and environmental research.
This project is expected to start in September 2027.
Before you apply: Please contact the main project supervisor, Prof Caroline Jay, to learn more about the project and discuss whether it may be suitable for you.
To be considered for this project you must submit a formal application through our online application portal by following the instructions provided here. Please note that you must select programme FSE PhD President’s Doctoral Scholarship
If you already have an applicant account, use this link to open an application for FSE PhD President's Doctoral Scholarship. If you do not yet have an applicantaccount, follow the account setup instructions and then use this link to open an application for the correct programme.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Applicants should hold, or be expected to obtain, a minimum Upper Second-Class Honours degree (or international equivalent) in a relevant discipline.
A background in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, Physics, Engineering or a related quantitative subject is desirable. Applicants should have an interest in machine learning and data analysis, and some experience of programming and computational methods.Experience in computer vision, deep learning, video analysis or related areas would be advantageous but is not essential.
