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Development of AI technologies to identify ocular disease


   School of Computing

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Dongxu Gao and Professor Zhaojie Ju

The work on this project will:

  • develop new deep learning techniques for effective disease prediction;
  • explore the generalisation of deep learning techniques;
  • access the trustworthiness of deep learning techniques.

Project description

Artificial intelligence assisted image interpretation is a rapidly advancing technology to quickly and accurately screen for ocular diseases, which would otherwise be a long and tedious process requiring more manpower at multiple levels. An efficient population screening method can help detect the disease early in asymptomatic people and prevent irreversible vision loss. The application of AI to automatically screen the suspected patients from the vast population would be a potential breakthrough in the current screening methods. This could help reduce unrequired hospital referrals, making way for patients with more severe eye conditions to be referred in time and hence reduce the patient waiting time. 

This project aims to develop the next generation of resource-efficient and reliable techniques to learn from massive medical and healthcare datasets, where a tremendous amount of data is generated every second and demands fast and accurate analysis. To this end, this project will focus on the following challenges: designing efficient methods to learn from multimodal data streams, where information come as different modalities such as images, text, sensory data, etc.; developing resource-efficient methods to reduce substantial costs of learning from massive datasets; designing reliable and safe learning algorithms with rigorous guarantees for safety-critical systems; and providing generalisation guarantee for the performance of deep neural networks trained on big datasets. 

The supervisory team has a good connection with the University of Liverpool and medical industries, which will provide professional networks needed to launch a career in next-generation AI, medical imaging and data science. This 3-year project will also provide essential training and equip the candidate with the needed skills to make a positive difference in society, the economy and beyond.

General admissions criteria

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

An ideal candidate must have a good degree (e.g. 2:1 or MSc) either in Mathematics (nonlinear optimization, calculus of variations, partial different equations, and iterative methods for nonlinear systems) or in computer science or data sciences (Python or C++ programming, machine learning and deep learning algorithms). Some previous research experience will be highly desirable but not mandatory.

How to Apply

We encourage you to contact Dr Dongxu Gao () to discuss your interest before you apply, quoting the project code below.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code:COMP5831023


Funding Notes

Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK students only).

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