About the project
This project will investigate how medical imaging, multimodal and longitudinal health data can be used to model individual health trajectories, identify disease risk earlier, predict future outcomes, and support personalised prevention through trustworthy, causal and data-driven artificial intelligence.
Healthcare is increasingly moving from reactive treatment towards earlier prediction, prevention and personalised intervention. Artificial intelligence has an important role to play in this transition by learning from complex medical data to better understand individual health trajectories, disease risk and future outcomes.
This project will develop advanced methods in Artificial Medical Intelligence for personalised and preventive healthcare. The research will focus on intelligent models that can integrate and learn from rich health information, including medical imaging, multimodal measurements and longitudinal data.
Key research themes include:
- personalised risk modelling
- prediction of disease progression
- multimodal representation learning
- longitudinal machine learning
- causal and counterfactual reasoning
- generative modelling and digital twins
Particular attention will be given to developing AI systems that move beyond population-level prediction towards models capable of representing individual patients, their evolving health states and potential responses to interventions or preventive strategies. An important part of the research will also be the development of reliable and trustworthy AI, including uncertainty estimation, robustness, interpretability and rigorous evaluation of personalised predictions and counterfactual outcomes.
You'll join the Advanced Technologies for Translational AI Research (ATTAR) Lab, an active research environment focused on translational artificial intelligence for healthcare, and you'll work alongside PhD students, postdoctoral researchers and academic and clinical collaborators. You'll receive advanced research training in artificial intelligence and machine learning for healthcare, including:
- deep learning
- computer vision
- multimodal learning
- longitudinal modelling
- causal and counterfactual inference
- generative modelling
- trustworthy AI
Training will also include scientific communication, reproducible research, high-performance computing and publication in leading international venues.
The School of Electronics and Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
