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  Controlled Synthesis of Virtual Patient Populations with Multimodal Representation Learning

   Department of Computer Science

  ,  Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

This project invites PhD candidates who are interested in developing generative models that can deal with complex multimodal patient data, contributing to safer, better and faster innovations of medical products via in-silico trials.

In-silico clinical testing/trials (ISTs) are an emerging approach to produce evidence for medical product innovation and regulation. Key to delivering ISTs are generative models, which can produce controlled virtual populations from vast real-world data sources. The generated virtual populations can expand/enrich the diversity of anatomical and physiological scenarios under which novel treatments can be tested within the in-silico trial framework. This thus ensures a broader and more equitable coverage of safety and efficacy for a given medical device.

You will work with multimodal data coming from radiological imaging, electronic health records, and wearable sensor data, etc. The synthesised virtual patient populations will comprise plausible instances of anatomy (from imaging) and physiology (from wearable sensors), and will not be traceable to any specific real patient data. The synthesis will be controllable, e.g., to impose specific conditions on the target virtual population to reproduce concrete inclusion and exclusion criteria in subsequent in-silico trials. You are expected to address modelling challenges in multimodal synthesis by considering at least one of the following aspects:

·      Limited availability of data (e.g., missing modality and insufficient patient instances) with potential inclusion biases.

·      Effective incorporation of domain medical knowledge in the synthesis (e.g., constraints arising from anatomical or physiological


·      Novel synthesis performance metrics that qualify and quantify the plausibility and representativity of synthetic populations.

·      Patient privacy protection and proven model trustworthiness.

The solutional development will build on state-of-the-art techniques in machine learning and computer vision areas, including but not limited to generative modelling, multimodal learning, zero-shot/few-shot learning, geometric deep learning, manifold learning, differential privacy. You will have access to data resources available through INSILEX and INSILICO Programmes. The supervisory team has a strong record of research success in machine learning, medical imaging and computer vision in general, and supported by a unique cadre of clinical experts that will provide contextual guidance to the student.

We are eagerly inviting strong and passionate applicants who have:

·      High interest in this project, preferably being experienced in some of the above mentioned machine learning or computer vision areas.

·      An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.

·      Very good experience with computer programming of mathematical, imaging and/or machine learning models and algorithms.

·      Excellent report writing and presentation skills.

·      Excellent ability to communicate with fellow students and colleagues, and importantly medical experts.

Before you apply:

Qualified applicants are strongly encouraged to informally contact the supervising academics Dr. Tingting Mu () and Prof. Alejandro Frangi () to discuss your application and research proposal prior to applying.



Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?

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.

You may also need to provide an English Language certificate (if applicable).

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 .


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.

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).


Computer Science (8) Mathematics (25)

Funding Notes

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. Please see the project description for more information.

How good is research at The University of Manchester in Computer Science and Informatics?

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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