Career Opportunities at Queen's

We invite applications for a Research Fellow in Computer Vision to join an exciting interdisciplinary project between the Centre for Intelligent, Sustainable Computing and the Centre for Public Health. This role offers the opportunity to build on our extensive track record and ongoing work in developing AI solutions for healthcare, while contributing to the next generation of predictive tools for age-related vision loss.

Age-related macular degeneration (AMD) is a leading cause of sight loss, with conversion to wet AMD (wAMD) in the second eye posing a major risk for patients. Monitoring this progression through Optical Coherence Tomography (OCT) imaging places increasing pressure on the NHS, often requiring substantial resources for eyes that may remain unaffected for years. Predicting which eyes are truly at risk is therefore critical.

The successful candidate will develop a novel AI model capable of predicting fellow-eye wAMD conversion from longitudinal OCT images. This will include identifying key biomarkers, designing algorithms that capture temporal change, and incorporating clinician-informed explanations to build trust and transparency into predictions.

Working closely with experts in ophthalmology, computer science, and public health, you will also collaborate with software engineers to rigorously test and containerise the developed model, ensuring it can be deployed within existing clinical frameworks. This is an excellent opportunity to contribute to a high-impact, interdisciplinary project that combines methodological innovation with practical application, ultimately helping clinicians prioritise care and protect patients’ vision.

The successful candidate will develop novel deep learning approaches to model longitudinal sequences of high dimensional OCT images, identifying biomarkers for conversion of wet AMD (wAMD) to the fellow eye. These models should be interpretable and provide intuitive explanations (e.g. using explainable AI methodologies). Code development should follow best software practices, in close collaboration with our software engineers to translate research code into demonstrable software. As an active research role, the postholder will need to stay up to date with developments in their specialism and related disciplines, contributing to team progress reports, international conferences, and journal publications.

About the person:

To be considered for this post, applicants must demonstrate that they meet the following criteria:

• Have, or is about to receive a PhD in Computer Science or other relevant fields relevant to the technical areas of the post
• Recent relevant research experience to include:
  • Experience developing novel deep learning methodologies (e.g. transformer models, Convolutional Neural Networks, Auto-encoder models, or LSTM networks).
  • Demonstrable ability to develop analysis pipelines for image data and knowledge of developing machine learning experiments with common challenges (e.g. noisy data, imbalanced datasets, missing data, etc.)
  • Experience in conducting research in machine learning and a publication record in line with stage of career in prestigious leading journals/conferences.
  • Working effectively as part of a research team in the development and promotion of the research theme.

To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information.

This post is available for 11 months. Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.

What we offer:

Beyond a competitive salary, the University offers an attractive benefits package including a holiday entitlement of up to 8.4 weeks a year, pension schemes and development opportunities. We support staff wellbeing with flexible working options, work-life balance initiatives and support for physical and mental health. You can find more detail on all of this and more at https://www.qub.ac.uk/directorates/HumanResources/pay-reward-and-benefits/ 

Queen's University is committed to promoting equality of opportunity to all. We subscribe to Equality Charter Marks such as the Diversity Charter Mark NI and Athena Swan and have established staff networks such as iRise (Black, Asian, Minority Ethnic and International Staff Network) and PRISM (LGBTQ+) which help us progress equality.

For further information on our commitment to Equality, Diversity and Inclusion, please visit www.qub.ac.uk/diversity. 

If you are an international applicant and don't already hold a visa that permits you to take up the role you are applying for, please use the information provided on our website to self-assess whether the University is likely to be able to support a visa application - http://go.qub.ac.uk/internationalstaffsupport 

Candidate Information