Event modelling concerns the analysis of ordered, timestamped data, with discrete states (or patterns) occurring at a particular point or points in the sequence. Examples include detecting faults or cyber-attacks in industrial processes and computer networks, predicting risk of fractures or disease flares in patients, learning of socio-political events through analysis of text data and even modelling the rate of taxi demand and hospital admissions.

The Data Science and its Applications research group is looking for a postdoc for a project on AI methods to high-dimensional biomedical data to promote understanding of biological processes associated with cardiovascular illness.

Interested in advancing your data science or machine learning career? The DSA group is recruiting new research assistants (“HiWis”) to work with us on several exciting projects. Positions are available immediately.

University Medical Center Mainz and TU Kaiserslautern are looking for a postdoc for a project on AI methods to high-dimensional biomedical data to promote understanding of biological processes associated with cardiovascular illness and Sars-CoV-2.

Survival/time-to-event analysis is an important field of Statistics concerned with understanding the distribution of events over time. Survival analysis presents a unique challenge as we are also interested in events that do not take place, which we refer to as ‘censoring’.