MPhil/PhD Studentship in Machine Learning and Progression in Diabetic Retinopathy

Investing in excellence: studentship opportunities

The University of Derby has an opportunity for a full-time postgraduate research studentship in the Data Science area of research.

Qualification type: Location: Funding amount: Hours: Closes: Interview: Start date:
MPhil/PhD Derby, UK £17,668 stipend pa + UK home tuition fees Full-time Midday on 6 January 2023 23 January - 1 February 2023 June 2023

The successful applicant will receive a maintenance stipend (based on the minimum stipend defined by UKRI, currently £17,668 for the academic year 2022/23) and home MPhil/PhD tuition fees only up to the target submission date.

Please note, if your application is successful and you are assessed as Overseas for fees purposes, you will need to pay the difference between the Home fees and the EU/Overseas fees.

The intended intake period is June 2023, or the next available intake. 

The successful applicant will be expected to complete their MPhil/PhD within 3 years on the MPhil/PhD route, contribute to the College of Science and Engineering REF submission and get involved in the wider research activities of the College. 

Applicants will become part of a friendly and welcoming team and will be supported and managed by their supervisors. 

The vacancy details are as follows: 


This studentship is to strengthen research and development in health informatics, one of the three research clusters identified within the data science research theme. It is also aimed at developing our relationship with the University Hospitals of Derby and Burton working with Patrick Richardson, a consultant ophthalmologist and a visiting Professor at the University of Derby.

The aim of this research is to calculate the individual risk of developing or progression of diabetic retinopathy using machine learning and deep learning models. The objectives are:

Project description 

Due to the highly complex nature of medical data and the relatively non-deterministic nature of the process of diagnosis, acquiring accurate and comprehensive risk has been difficult to achieve. We, therefore, propose using Graph-based Reinforcement Learning (or similar deep learning models), a method of AI that is able to learn incrementally from data and the environment. The three types of data involved i.e. clinical, image and visual acuity will be integrated into machine-readable graphs and reinforcement learning will be applied to iteratively calculate a score for developing retinopathy. The data sources will be integrated into a single consolidated knowledge base (i.e.; Graph) that will be iteratively analysed and features will be learnt using Graph-based Reinforcement learning for estimating and classifying the risk of Diabetic Retinopathy in different populations and providing personalized treatments. 

The ideal candidates should have an excellent grasp of modern machine learning and deep learning models and possess good skills in fast prototyping of new solutions and other aspects of research. Previous programming experience, preferably in python and/or Matlab, is required. A very good BSc or preferably an MSc degree in computer science or AI/data science is required. Excellent communication skills are essential for the candidates to communicate effectively with the research team and medical domain experts.

Potential project impact 

The PhD studentship will contribute to the development of a novel, accurate and comprehensive risk prediction in the progression of diabetic retinopathy. When completed, the solution will be developed into a product that could be used by local health authorities and as the basis for further funding. Given the novelty of the proposed research and solution, it is expected that high-quality papers will be produced. This will benefit future REF submissions and possibly impact case studies if further external funding is secured.

Principal accountabilities and responsibilities

The successful applicant is expected to:

To apply 

Completed applications should be submitted via our online application system quoting funding reference: S&E_Machine Learning_1122

For other enquiries which are subject-specific please contact Professor Farid Meziane ( 

Closing dates for applications: Midday on 6 January 2023 

Interviews: 23 January - 1 February 2023

If you have not been invited for an interview by the interview date, please assume your application has been unsuccessful.  

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