The development of Digital Twins represents a powerful approach toward personalized and predictive healthcare. A Digital Twin is a virtual representation of an individual patient that can be used to simulate, predict, and analyze disease progression as well as potential therapeutic interventions based on real-world patient data.
This medical doctoral thesis / master thesis focuses on the development of a Digital Twin framework based on a comprehensive patient database. The project aims to integrate multimodal, individual-level data—including clinical characteristics and proteomic profiles—with population-level information such as clinical trials and cohort studies. Using advanced machine learning approaches, a multiscale and multimodal data model will be established to capture complex biological and clinical interactions and to enable data-driven predictions relevant for clinical decision-making.In this project, high-level computational methods and machine learning techniques will be applied to translate this concept into a clinically meaningful medical application.
The thesis will be supervised in close collaboration between the Department of Neurosurgery, University Hospital Mannheim (PD Dr. Miriam Ratliff) and the Quantum Dynamics of Atomic and Molecular Systems group, Heidelberg University (Prof. Dr. Matthias Weidemüller). Mobility between the Mannheim and Heidelberg campuses is a prerequisite and an integral part of the project. The position is a 9-month full-time research project, requiring high motivation, strong commitment, and pronounced computational affinity.