To solve complex real-world tasks we develop machine learning solutions using state-of-the-art supervised Deep Learning methods as well as self-supervised approaches and evaluate their quality along different quality dimensions.
Central application areas of our research lie in medicine and the Sciences, where we develop solutions for real-world problems involving different data modalities. Together with practitioners and other users we always strive for developing the best solutions.
Climate change research is a vast and significantly important research topic. Together with partners from our research network we strive for developing machine learning-based methods that can be helpful to push further research developments in this field.