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  Application of machine learning algorithms to medical images and clinical/DNA data

   Department of Electronic and Electrical Engineering

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  Dr Fumie Costen  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

This project will develop an automated classifier or the estimation of prognosis of the illness using medical images of the human body, clinical data, and gene information. The project shall make use of the machine learning algorithms such as multi-layer neural networks, the random forest, the support vector machine, principal component analysis, and convolutional neural network. The tuning of hyper-parameters and the architecture will be carried out based on the Bayesian optimisation. The computation might be carried out in one of the fastest supercomputers in the world for certain programming languages, applying the message passing interface parallelisation for the distributed memory architecture and OpenMP parallelisation for the shared memory architecture and hybrid system of these. The ultimate goal is to produce a green-AI system which requires the minimum input data to achieve comparative accuracy and many associated high-quality journal papers.

Applicant requirements

Applicants should have or expect to achieve at least a 2.1 honours degree in computer science or electrical engineering.


At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers. 

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for. 

Before you apply 

We strongly recommend that you contact the lead supervisor for this project before you apply. 

How to apply 

To be considered for this project you’ll need to complete a formal application through our online application portal

When applying, you’ll need to specify the full name of this project, the name of your supervisor, how you’re planning on funding your research, 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.  

If you have any questions about making an application, please contact our admissions team by emailing [Email Address Removed]

Equality, diversity and inclusion 

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). 

Biological Sciences (4) Computer Science (8) Engineering (12) Mathematics (25)
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 About the Project