Machine learning for early and personalised risk assessment for pathological myopia

This project aims to assess if vascular analysis in retinal images can be used for personalised and early myopic degeneration/pathological myopia risk prediction. The retinal image data will be combined with clinical measurements of refractive error to develop a comprehensive AI based model that evaluates the risk of irreversible vision loss due to pathological myopia.

The primary aim of this project is to enable the development of a risk assessment tool to facilitate early and personalised interventions for pathological myopia using non-invasive imaging of the eye. The technology that will be developed will have direct translational applications for risk assessment in various other diseases including retinal disorders, glaucoma, Alzheimer. This project directly aligns with two themes of EPSRC: (i) AI and data-science for engineering, health, and government (ASG); (ii) Clinical technologies. This project has the potential to reduce irreversible blindless caused by refractive error by identifying patients who are at a higher risk and providing more rigorous myopia management.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. 

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply. Please send the supervisor (Ajay) your CV to discuss suitability.

How to apply

Apply online through our website: https://uom.link/pgr-apply-fap

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

EDI

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).