(PhD by Enterprise) Automated Blink‑Dynamics Analytics: AI System Development and Translational Planning

About the Project

This PhD by Enterprise project offers an exciting opportunity to work at the intersection of artificial intelligence (AI), clinical vision science, and digital health innovation. Diabetic neuropathy is a major complication affecting up to half of people with diabetes, yet current screening tools detect it only at late stages. The eye provides a unique window into early neural dysfunction. Recent work at the University of Manchester shows that high‑speed infrared imaging can reliably capture detailed blink‑behaviour signals, providing a rich foundation for exploring their potential diagnostic value.

The student will help develop and validate an AI system capable of automatically analysing high‑speed videos of human blinking. You will collect and annotate large datasets, develop machine‑learning models to automatically extract blink metrics, and explore how blinking differs across healthy, diabetic and diabetic neuropathy populations. Alongside this scientific work, you will receive structured entrepreneurial training and investigate how the technology could evolve into a deployable clinical product. This will involve exploring hardware options, clinical workflow integration, user needs, market opportunities, and routes to commercialisation.

Working closely with experts in AI‑based medical image analysis, clinical optometry, and ophthalmology, you will gain experience across data science, algorithm development, clinical research, and innovation. The project has strong translational potential, and the student will play a central role in shaping both the scientific and entrepreneurial direction of the work.

Key Research Activities:

•  Collect blink‑video data from healthy volunteers and patient groups.
•  Develop AI system for eye feature landmark detection and blink‑metric extraction (based on BoneFinder®).
•  Evaluate blink‑based biomarkers in diabetes populations.
• Evaluate the impact of video technology with varying frame rates.
•  Explore hardware and workflow requirements for clinical deployment.
•  Conduct market discovery and contribute to IP/commercialisation planning.
•  Produce a thesis combining scientific research with a business plan.

The student will join a supportive and inclusive research environment that values collaboration, innovation, and real-world impact. They will gain extensive experience of working in an interdisciplinary team, and work alongside researchers and students studying a range of different image analysis problems. This project is ideal for candidates passionate about AI in healthcare, digital diagnostics, and translational innovation.

Entry Requirements

Applicants should hold (or be about to obtain) a First or Upper Second class (2:1) UK honours degree, or international equivalent, in a relevant subject.

Application Guidance

Candidates must contact the primary supervisor before applying to discuss their interest in the project and assess their suitability.

Apply directly via this link: https://shorturl.at/BRKKT or on the online application portal, select PhD by Enterprise Programme as the programme of study.

Please ensure that your application includes all required supporting documents:

Curriculum Vitae (CV)

Supporting Statement

Academic Certificates and Transcripts

Incomplete or late applications will not be considered. Further details are available on our website: PhD by Enterprise | Biology, Medicine and Health | The University of Manchester

Equality, diversity and inclusion are central to the University’s activities. The full statement can be found here: https://www.bmh.manchester.ac.uk/study/research/getting-started/equality-diversity-inclusion/

Funding Notes

This PhD by Enterprise studentships provide full funding for tuition fees and a stipend at the UKRI rate for four years starting in September 2026. This funded scheme will also allow for Visa and Immigration Costs to be reimbursed for successful international PGRs.