| Research topic | Enhancing structural health monitoring with eXplainable Artificial Intelligence |
|---|---|
| Reference number | FNS_BAlBander_February 24 |
| Overview | This PhD project aims to leverage the capabilities of the eXplainable Vision Transformer (ViT) architectures, an innovative model in the field of artificial intelligence and computer vision, to identify structural damage from fire data and predict the behaviour of buildings during fires, ultimately leading to the construction of safer and more fire-resistant structures. The data required for this project will be supplied by an external Canadian partner. The datasets will comprise images and videos demonstrating the testing of a new blue light technique that enhances the clarity of images/videos taken during fire situations. Please quote FNS_BAlBander_Feb 24 on your email.
|
| Details | See advert and details |
| Duration | 3 years |
| Fees | Self-funded: Please note that self-funded applicants must provide funding for both tuition fees and living expenses for the 3-year duration of the research. There is a future possibility of competitive scholarship awards for outstanding applicants. However, none are currently available. For information regarding University tuition fees, please see: http://www.keele.ac.uk/pgresearch/feesandfinance/ Students are also provided with access to Faculty research training funds for research related expenses including - but not limited to - conference attendance, external training courses. |
| Stipend | Not applicable (self funded PhD) |
| Closing date | Applications accepted all year round |
| Apply | |
