PhD Studentship University AIDIGFIRE project

This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.

All applicants will receive the same stipend irrespective of fee status.

Final date to receive applications:
Midday (UK Time) on
Wednesday 17
th
September 2025
for a start date of 2nd February 2026.

To apply, please follow the below steps:

• Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project  will be required to upload your proposal in place of a personal statement on the BCU online application form.
• Upload two references to your online application form (at least one of which must be an academic reference).
• Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
• International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here . Please check the individual research degree course page for the required scores.

Fires in high-rise buildings can be challenging and difficult to manage and suppress. In recent years, fires in high-rise buildings have caused disasters resulting in death and injury, such as the Grenfell Tower fire in London in 2017, where 72 lives were lost. Regardless of what caused the fire, the fire service faced difficulties in spotting fires in the external cladding, which led to delays in identifying the fire’s location and, more importantly, delays in shifting from a stay-put strategy to full simultaneous evacuation due to the evolving situation.

Therefore, having integrated systems that alert the fire service to fire conditions and occupant locations, and allow monitoring of fire and smoke progression through the building, is extremely essential.

This project aims to address this issue by developing a coupled AI–digital twin system that displays essential building fire safety information and provides real‑time predictions of fire and smoke conditions in the building, as well as occupant locations and the status of the evacuation process. These capabilities include extracting data from fire detection and protection devices in the building, monitoring evacuation routes, and estimating the number of evacuees and occupants remaining inside.

The project will utilise advanced fire and smoke dynamic simulations to train machine learning models to understand how fire and smoke behave in a building. Once trained, the models will be able to predict the likely spread of fire and smoke based on real-time sensor inputs such as heat or smoke detectors. In addition, evacuation conditions will be monitored by training machine learning‑based detection algorithms to count the number of occupants leaving the building and identify the locations of remaining occupants.

This information will be visually mapped onto a digital model of the building for easy viewing and interpretation. The resulting digital twin platform will continuously update and display the current fire conditions in the building.…