About the Project
Engineered timber is increasingly used in modern construction because of its structural efficiency and potential contribution to more sustainable buildings. However, understanding how timber structural systems behave following fire exposure remains an important challenge, particularly when decisions are required about their residual performance and future use.
This PhD project will investigate the behaviour of engineered timber following fire exposure using a combination of experimental testing, advanced imaging, numerical modelling and data-driven methods.
The research will involve controlled laboratory testing of engineered timber specimens before and after thermal exposure. Advanced optical measurement and image-processing techniques will be used to monitor deformation and damage development during structural testing. Numerical models will be developed to improve understanding of the observed structural response and to investigate parameters that influence post-fire behaviour.
The project will also explore the use of artificial intelligence and machine-learning techniques to analyse experimental and image-based data and identify relationships between observable damage and structural performance.
The successful candidate will work across experimental and computational structural engineering and will develop skills in areas including structural fire engineering, timber engineering, Digital Image Correlation, image processing, finite-element modelling and artificial intelligence.
The overall aim is to improve understanding of the residual behaviour of engineered timber after fire and contribute towards more informed approaches for the assessment of timber structures following fire exposure.
This project is expected to start in September 2027.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply.
How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships. If you don’t already have an applicant account, please follow the instructions here.
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 the 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.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
Eligibility: The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD such as Civil Engineering, Structural Engineering, Mechanical Engineering, Materials Engineering (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD such as Civil Engineering, Structural Engineering, Mechanical Engineering, Materials Engineering (or international equivalent). Interest in structural engineering, structural fire engineering, timber engineering, experimental mechanics, numerical modelling, computer vision, image processing or machine learning is encouraged and previous research experience in Digital Image Correlation, finite-element modelling/Abaqus, image processing or machine learning would be advantageous but is not essential. Appropriate training will be provided. Candidates should demonstrate strong analytical ability, an interest in experimental research, willingness to undertake laboratory testing, and good written and verbal communication skills.
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoE
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently £21,805/annum) and up to a £2.5k/annum research training support grant for the full duration of the 3.5-year programme.