Helene Rytgaard

The Department of Public Health at the University of Copenhagen, in collaboration with the Pioneer Centre for SMARTBiomed, offers a postdoctoral fellowship focused on causal inference and statistical methodology. The position sits in the Section of Biostatistics, an internationally recognised research environment with strengths in time-to-event analysis and broader expertise in causal inference, latent variable modelling, and prediction models. The project is centred on methodological research at the intersection of causal inference, event-history analysis, statistical machine learning, and semiparametric theory. The successful candidate will work on statistical methods for longitudinal and time-to-event data, with special emphasis on continuously monitored data, dynamic treatment strategies, identification, and instrumental variable methods. Development of methods for causal inference with continuously observed treatment processes and longitudinal data structures Instrumentation of survival and competing risks analysis with instrumental variable methods Semiparametric and machine-learning-based estimation for time-varying treatment effects Efficient and robust estimators for complex causal effects Software implementation, simulation studies, and application to biomedical research questions The role is embedded in the interdisciplinary research environment of SMARTBiomed, whose mission is to advance methods and software for inference from large-scale human data and to support applications in medicine. The fellow will contribute both theoretically and computationally, collaborate on publications, and may also help build software outputs that increase the utility of the research. The position also allows scope to develop independent research directions aligned with the centre’s goals, participate in conferences and research meetings, and engage with the wider research group at the University of Copenhagen. Teaching opportunities at graduate or undergraduate level may arise during the fellowship. The anticipated start is 1 March 2027, or as soon as possible thereafter.