About the project
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 BOB体育登录网址_欧宝体育官网平台-APP|下载, 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.
