Using machine learning to model complex biological interactions

About the Project

Supervisory Team: Dr Mohammad Soorati and Prof Stephen Beers

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.

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

Apply now

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)