The main research areas revolve around control theory, algorithm development, and their interplay. This includes analytical criteria for characterizing the dynamic behavior (e.g., stability) of different categories of systems (hybrid, networked, stochastic, parameter-distributed), and corresponding optimization-based approaches for model, data and learning based control, prediction or estimation techniques of system or process behavior over a finite time horizon. The Institute has evolved through interdisciplinary research. Its goal has been to use modern control engineering methods and tools to facilitate scientific and technological progress in various application domains from a systems theory and control perspective, including energy, robotics, mobility, biology, and chemical engineering. It also addresses the hybridization aspect, referring to the fusion of different domains, combining model- and data-driven methods and algorithms, merging different system classes or descriptions, and integrating control engineering and machine learning methods.