DT4Health brings together a world-class multidisciplinary team of supervisors to train future innovation leaders to articulate and materialise the Digital Twin vision in healthcare. By establishing a strong, interdisciplinary, and inter-sectoral training network, we educate researchers and practitioners to have both a focused specialty and the ability to bridge between diverse disciplines and three core subject areas:
- Mathematical foundations: building a Digital Twin requires continuously matching inductions and deductions from data. Our students learn and advance mathematical mechanistic models of healthcare systems, and the statistical theory for personalising these models, quantifying the inherent uncertainty and allowing forecasting.
- Engineering technologies: updating a Digital Twin requires acquiring new data and integrating multiple data streams in large-scale simulations. Our students learn about state-of-the-art sensing and imaging technologies, and how to exploit the unprecedented computing capacity that is available nowadays to run model inferences and help decision making. DTs are complex cyber-physical systems and our students will learn how to engineer these from a software and systems perspective.
- Medicine: Our students develop a deep understanding of key concepts of human physiology, specific problems in the management of disease, and the actual needs and limitations of our healthcare system.
We also provide our students with a set of horizontal skills and targeted commercialisation workshops coordinated with the King's College London Centre for Doctoral Studies. The specific selection of training events is determined based on an individual annual training-needs review undertaken by the student in consultation with their supervisory committee. Students have the opportunity to undertake a collaborative mini-project with other students crossing knowledge areas.
The program provides a cohort-based education built around individually focused research projects. Individual projects help build deep expertise and create value, while the cohort-based approach enables knowledge exchange across areas and trains students in inter-disciplinary work and communication.