Available
Project number:
2025_19
Start date:
October 2025
Project themes:
Main supervisor:
Senior Lecturer in Statistics
Co-supervisor:
Prof. Maddy Parsons, Professor of Cell Biology, Randall Centre for Cell & Molecular Biophysics
Additional Information:
Advanced statistical methods for multimodal bioimages in cancer research
This cross-disciplinary project has been driven by an urgent need for new and innovative approaches to understand and interpret multimodal spatial biology data. Multimodal bioimaging consists in the integration of several imaging techniques to obtain a comprehensive view of biological structures and processes. For example, combining spatial transcriptome, DNA organisation, proteome, metabolome and metallome of tissues. Analysis of multimodal bioimages has the potential to enhance our understanding of complex biological systems and disease mechanisms. An important example is the exploration of why patients with triple negative breast cancer (TNBC) do not respond to chemotherapy. There is currently no means to predict which patients will respond to treatment, because the mechanisms of drug-resistance are unknown. Thus, urgent new approaches are required to identify predictive biomarkers and alternative therapies for non-responder TNBC patients, and multimodal bioimaging of tissues can play an important role. However, multimodal bioimaging is an emerging field, and no tools exist to currently undertake such complex, high-dimensional analysis. The project will develop new statistical methodologies based on scalar-on-image regression to analyse multimodal bioimages from cancer research. The aims include a) the extension of scalar-on-image regression model to the multimodal case, b) the development of model selection and uncertainty quantification procedures for these new models, c) the application of these models to multimodal bioimages of tissues from breast cancer patients and d) establishing an effective way to communicate the results of the analysis to biomedical researchers and support them in drawing conclusions.
We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 6 February 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in April 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at