Artificial Medical Intelligence for precision and preventive healthcare

About the Project

Supervisory Team: Dr Rahman Attar

This PhD project focuses on Artificial Medical Intelligence, creating AI tools that anticipate risks rather than respond after disease develops. By integrating real-world health data, you will develop digital twins and predictive models to guide prevention and personalised care, combining machine learning innovation with clinical collaboration to improve outcomes.

Modern healthcare is rich in data but limited in foresight. Medical imaging, clinical records, and wearable technologies generate vast amounts of information, yet most health systems remain reactive—intervening only after disease has developed.

This PhD project addresses this challenge by developing Artificial Medical Intelligence (AMI) methods to move healthcare towards prediction, prevention, and personalisation.

The research will focus on designing digital twin models—virtual representations of patients that simulate health trajectories and anticipate risks before they manifest. By integrating multimodal data, from imaging to clinical and demographic records, you will develop AI models capable of identifying early warning signals, supporting targeted interventions, and improving patient outcomes.

The intended outcomes are both technical—advancing the state of the art in multimodal machine learning—and translational, with a clear route to clinical application.

Students will join the Advanced Technologies for Translational AI Research (ATTAR) Lab in the School of Electronics and Computer Science (ECS) at the University of Southampton, working in close collaboration with hospitals and clinicians.

Training will cover advanced machine learning, data science, and clinical translation, supported by interdisciplinary supervision. You will have access to state-of-the-art high-performance computing facilities and opportunities to engage with healthcare partners, ensuring the research has real-world impact.

This project is ideal for applicants with a background in computer science, engineering, or data science who are motivated to apply their skills to healthcare challenges with significant societal importance.

Entry Requirements

You must have a UK 2:1 honours degree, or its international equivalent in

• computer science
• engineering
• data science
• or relevant disciplines.

It is essential that you have

• strong programming ability
• practical experience in developing AI models or computational systems.

Applicants without an MSc or MEng in a closely related area must provide clear evidence—such as research experience, advanced technical skills, or publications—that they are capable of successfully completing a PhD in this field.

Closing date: 31 July 2026. Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding: We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.

Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.

Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

For more information, please visit our postgraduate research funding pages.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk)

You need to:

• choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Computer Science (7089)
• add name of the supervisor in section 2 of the application

Applications should include:

• research proposal
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification

For further information please contact: feps-pgr-apply@soton.ac.uk