Website The University of Southampton
About the project
This project aims to build an AI-driven system that analyses live-cell microscopy videos showing how immune cells attack cancer cells. The videos are generated in a biology lab where each experiment can be precisely controlled. You will create machine-learning and computer-vision algorithms that can detect, track, and model these cell-to-cell interactions, revealing patterns that explain when and why immune cells succeed or fail.
Antibody-based treatments have transformed how certain cancers are treated, offering highly targeted ways to attack malignant cells. Yet, even with the same type of therapy, patient responses vary widely. In some cases, immune cells effectively eliminate the cancer cells, while in others, the same treatment has little effect. This inconsistency points to complex, dynamic interactions between immune cells and cancer cells that remain poorly understood. Traditional experimental techniques capture only snapshots of these interactions, missing the rich temporal and behavioural patterns that unfold over time.
This project will use advanced artificial intelligence to help uncover what drives these differences. You will work with time-lapse microscopy videos showing immune cells interacting with cancer cells, developing computational methods to analyse, model, and explain their behaviour.
The project combines modern machine learning, video analysis, and explainable AI to identify subtle cues and temporal dynamics that influence treatment outcomes.
You will design algorithms that can detect meaningful behaviours, learn from complex visual data, and provide insights that go beyond what human observation can achieve. The work will be carried out in close collaboration with experimental biologists who generate the data, but the project will primarily focus on the computational side developing, testing, and refining AI models to interpret biological processes.
The School of Electronics & Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break.
The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
- computer science
- Artificial Intelligence
- biology
- biomedical sciences
Strong computational or data analysis skills are essential.
Desirable skills:
- proficiency in Python and modern machine learning frameworks
- experience or interest in computer vision
- foundational understanding of biological concepts
Fees and funding
This project is fully funded by the Savvas Chamberlain Scholarship.
How to apply
You need to:
- choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Computer Science (7089)
- add name of the supervisor in section 2 of the application
Applications should include:
- research proposal
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
If you have a general question, email our doctoral college (feps-pgr-apply@soton.ac.uk).
Project leader
For an initial conversation, email Dr Mohammad Soorati (M.Soorati@soton.ac.uk).
Want fewer missed deadlines?
Follow a channel you care about (Graduate → Post-PhD).