The main research interests of Janine Weber-Hamacher are the development and efficient implementation of hybrid methods which use machine learning algorithms to improve existing numerical methods and vice versa within the field of scientific machine learning (SciML). One exemplary application within this field is the development of robust and efficient adaptive domain decomposition methods for the iterative solution of heterogeneous problems, as, e.g., arising in solid mechanics. Here, learning-based methods are used to reduce the computational cost of the respective approach while still maintaining the robustness of the iterative solver. Additionally, Janine Weber-Hamacher is interested in the theoretical analysis and parallel implementation of efficient preconditioners for the solution of large scale problems which are highly parallel scalable on modern multi-core computers. In particular, she works on adaptive, multi-level, and approximate domain decomposition methods which scale up to many thousand of cores. Finally, Janine Weber-Hamacher is interested in the integration of new SciML approaches in high performance computing (HPC) software for modern computer architectures. Here, one exemplary application is the fast and accurate classification of large-scale image data, by integrating knowledge from domain decomposition and efficient parallelization into classical learning-based methods.