University of Nottingham PhD: Multi-mode microscopy to probe bacteria-surface interactions
Closing date: Friday 3 July 2020
- The stipend is £15,285 and is tax free for eligible EU/Home candidates
- High first degree (2.1 or above) in a related subject eg. Physics, Biological and Health Sciences, Data Science, Computer Science etc.
- European/UK Students Only
About the project
A PhD studentship is available at the University of Nottingham funded by GlaxoSmithKline Consumer Healthcare. This studentship will be based in the School of Pharmacy in a collaboration with the School of Life Sciences.
Multi-mode microscopy techniques: Bacteria are well known to form surface-associated structure called biofilms. Biofilms are associated with antibiotic-tolerance, making them difficult to treat and a leading cause of infections globally, ranging from respiratory conditions like cystic fibrosis to oral health disease such as gingivitis. As such, there is a significant need, both within academia and industry, to develop methods of limiting biofilm formation and growth using detailed information on their formation.
Bacterial surface sensing and interaction plays a key role in the initiation and development of biofilms. The successful candidate for this PhD project will use various 2D and 3D microscopy techniques to visualize motile bacteria interacting with various coated surfaces. One key microscopy mode used in this project will be Digital Holographic Microscopy (DHM). DHM utilizes light interference patterns to enable 3D tracking of swimming of bacteria. This project aims to increase understanding of the modes of action of various anti-biofilm surface coatings relevant to consumer healthcare and the bacterial responses to them. The industrial collaboration with GSK means there is a direct link to real-world applications of this research. GSK is a science-led global healthcare company and the student will have the opportunity to spend time working at GSK. The successful candidate will be involved in both wet-lab work and data processing of large data sets.