AIDIGFIRE: Developing smart buildings fire safety management using an integration of artificial intelligence and digital twin technologies
Doctoral Training Grant Funding Information
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.
Application Closing Date:
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You 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.
Frequently Asked Questions
To help support you to complete your application, please consult the frequently asked questions below:
Project title: Developing smart buildings fire safety management using an integration of artificial intelligence and digital twin technologies
Project Lead: Dr Javad Hashempour
Project ID: 13 - 46456414
Project description:
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. During an emergency, this innovative tool will provide firefighters and building managers with critical, timely information to support better and faster decision-making, ultimately improving building fire safety and firefighting efforts.
Anticipated findings and contributions to knowledge:
This research is anticipated to deliver several important findings. The project will promote the use of building sensor data in machine learning–based real-time prediction models. It will establish the feasibility of using machine learning, trained on extensive fire simulation data, to rapidly and accurately predict the behaviour and spread of fire and smoke within high-rise buildings.
The significant contributions to knowledge include providing a novel, integrated approach to digital fire safety management. The developed digital twin will bridge gaps between static fire safety documentation, real-time fire detection and protection sensor data, and predictive modelling. This innovative framework will set a new benchmark for how fire safety information is managed, visualised, and utilised during emergencies.
Moreover, the research outcomes will advance the theoretical understanding of data integration in digital twin platforms. It will demonstrate practical methods to enhance building fire safety practices, improve emergency response capabilities, and inform future building fire safety regulations and standards, supported by abundant data from real fire incidents.
Person Specification:
We are seeking a highly motivated and capable candidate to support a project at the intersection of fire safety engineering, computational modelling, and emerging digital technologies. This position is ideal for graduates in computer science or computer/electronic engineering with experience in the application of machine learning. However, applicants with other relevant engineering backgrounds, such as fire engineering, mechanical engineering, architectural engineering, or civil engineering, with proven experience in applying machine learning will also be considered.
While an MEng or MSc qualification in a relevant field is desirable, equivalent industry experience with a BEng or BSc will also be considered. The successful candidate should have applied programming skills to develop, train, and validate machine learning models, along with a sound understanding of algorithm selection, model evaluation, and the interpretation of results. The candidate must demonstrate strong analytical and problem-solving abilities, particularly in working with complex datasets and interpreting outputs within the context of the built environment and fire safety.
A working understanding of digital twin technology and its applications, such as how digital twins integrate simulation, monitoring, and real-time data to support improved decision-making, is desirable but not essential. The ideal candidate will have experience working across disciplines, including fire safety engineering, building engineering, and computational modelling. Strong communication skills, both written and verbal, are important, as the role involves presenting complex technical information to both technical and non-technical audiences. Collaboration and teamwork are essential; the candidate should be confident working independently as well as part of a multidisciplinary team.
Overseas applicants:
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.
Contact:
If you have any questions or need further information, please use the contact details below:
- For enquiries about the funding or project proposal, please contact: javad.hashempour@bcu.ac.uk
- For enquiries about the application process, please contact: research.admissions@bcu.ac.uk