PhD researcher in explainable machine learning and survival analysis

The PhD project consists of two main parts. First part, is about fundamental research on the intersection between interaction learning and survival analysis. In particular, interaction learning deals with predicting or clustering interactions between two sets of objects. Survival analysis is rooted in statistics and deals with predicting the time until an event occurs. The focus is on the development of new algorithms for prediction and clustering in this context. Second, the algorithms will be applied in the context of developing smart alarms in an intensive care unit. Given the current overload of alarms, it will contribute to reduce alarm fatigue in clinical staff and alarm anxiety. For this purpose, the algorithms will be applied on real data from the local hospital.