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. The goal of your Master thesis is to investigate and develop effective optimization and training methods for the identification of electric machines. The focus lies on the design of physics-enhanced data-based models and the implementation of training algorithms. Of further interest is the evaluation of various optimization techniques and their impact on model accuracy and computational efficiency. During your Master Thesis your task includes the familiarization with the physical models of electric machines. You will be responsible for conducting literature research on existing ML-based approaches for the identification of electric machines. In addition, you will set up a differentiable parametric model of the electric machine. Furthermore, you will implement and compare different training and optimization methods, such as quadrature-based approaches, multi-step ODE solvers utilizing various integration techniques and prediction horizons, neural ODEs and methods involving the differentiation of measured currents. Last but not least, you will evaluate the training methods in terms of convergence speed, accuracy, robustness and computational cost.