Accidental fires remain a major threat to public safety and building resilience, with wide-ranging physical, psychological, economic, social, and environmental consequences. In response to the Grenfell Tower fire, the UK Government introduced the Building Safety Act 2022, establishing the Building Safety Regulator and enforcing stringent safety checks for high-risk buildings. Despite these measures, significant challenges persist in ensuring comprehensive fire safety, particularly during early building design and the development of effective evacuation strategies. Current regulations often do not address emerging risks from innovative materials, advanced systems, and modern construction technologies.

Traditional large-scale compartment fire tests provide valuable insights but are costly and time-consuming. Computational fluid dynamics (CFD) models are commonly used to support fire safety design, yet they are computationally intensive and can hinder timely assessments. Artificial intelligence (AI) models present innovative solutions, enabling rapid analysis of complex systems, pattern recognition, and accurate prediction of fire behaviour. By integrating physical laws into AI models, this research aims to simulate fire dynamics, heat transfer, fire toxicity, and structural responses with high precision, reducing reliance on expensive numerical methods.

This PhD project will address critical knowledge gaps in fire safety design by combining AI with advanced fire dynamics simulations. The research will advance fire dynamics predictions, streamline fire safety analysis, and uncover new insights through real-world fire data analysis. Historical fire incident reports and regulatory documents will be collected to understand fundamental fire dynamics, fire toxicity, and structural responses. AI models will be developed and trained on this data, then used to simulate real fire scenarios in diverse environments, including room fires and large-scale building fires. Model validation will be performed against experimental data, historical fire events, and traditional CFD simulation results.

The supervisory team brings complementary expertise in fire dynamics, material flammability, fire toxicity, structural response under fire conditions, numerical heat transfer, AI applications in fire safety, and computer modelling of fire phenomena. They have extensive publication records and collaborate with universities, research institutions, and industry partners worldwide. The research will span multiple disciplines, including engineering, chemistry, mathematics, and computer engineering, offering the student opportunities to work across these fields.

Applicants should have a strong academic background in engineering, computer science, mathematics, chemistry, or related disciplines, with interest or experience in AI, fire safety, computational modelling, or data analysis. The position is based at Ulster University - Belfast Campus within the Faculty of Computing, Engineering and the Built Environment. The application deadline is February 27, 2026. For further details and to apply, visit the official FindAPhD project page.