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Department of Computer Science

Interpretable machine learning for healthcare applications

Primary supervisor

Additional supervisors

  • Hongpeng Zhou

Contact admissions office

Other projects with the same supervisor


  • Directly Funded Project (Students Worldwide)

This research project has funding attached. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

There has been an increasing interest in developing interpretable machine learning (ML) approaches for healthcare applications, e.g., cancer research. The developed ML approaches can be implemented for disease diagnosis, prognosis, biomarker identification and biological mechanism discovery. This project aims to look for a doctoral student to focus on developing efficient and robust machine learning and deep learning algorithms, e.g., interpretable learning, Bayesian learning, generative modelling, and large language models. Several exciting research topics are available around the following healthcare applications, 1) Generative modelling for spatial transcriptomics; 2) Interpretable deep learning for biomarker identification; 3) Large language models for multi-omics data; 4) Machine learning modelling for drug effects research. The group will provide excellent opportunities for collaboration with ML researchers, biologists and clinical experts.

Person specification

For information


Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?