The identification of accurate simulation models of electric machines is a crucial step for the design of high-performing controllers, fault diagnosis, and many other tasks. Often, the time available for performing measurements on a physical device is limited. One general approach to still obtain an accurate model under such constraints is transfer- and meta-learning. We further aim to combine this with knowledge on the physics of the electric machine by forming a grey-box model, which promises to further reduce the required measurement time. The goal of this thesis is to investigate grey-box meta-learning approaches for accurate identification of models of electric machines, given tight measurement time constraints. During your thesis you will conduct comprehensive literature research on meta-learning and transfer-learning approaches. You will familiarize yourself with physical models of electric machines. Furthermore, you will design and implement meta-learning models, as well as train algorithms for the identification of electric machines. Finally, you will evaluate and benchmark the developed approaches through simulation.