The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Thus, it should not come as a surprise that Bayesian methods are increasingly used in statistical and computational inference in both science and industry. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. In our group, we are working on a wide range of research topics related to the development, evaluation, implementation, or application of Bayesian methods. This includes, among others, prior specification, model evaluation and comparison, uncertainty quantification, multilevel models, and simulation-based inference. If you are a scientist who wants to collaborate with us or a student who wants to write their thesis on topics related to Bayesian statistics, please reach out to us!