Modern power electronic systems, especially AC/DC and DC/DC converters, form the indispensable backbone of tomorrow's technology landscape: In e-mobility, they enable ultra-fast, highly efficient onboard charging systems and robust power delivery. In robotics, they drive highly dynamic, precise actuators and electric drives required for collaborative and autonomous systems. In data centers, they ensure maximum energy efficiency, high power density, and uninterrupted operation. To meet the stringent efficiency and space requirements of these applications, modern power electronics increasingly relies on advanced soft-switching topologies. While these topologies significantly reduce switching losses and maximize power density, they exhibit highly nonlinear dynamics. At the same time, cost and space optimization necessitate smaller energy storage components (capacitors and inductors), which further exacerbates system instability and complex dynamic behavior. Traditional linear control methods (such as standard PI controllers) struggle to maintain optimal performance under these demanding conditions.

This master's thesis aims to investigate, compare, and implement next-generation closed-loop control algorithms to manage these nonlinearities and further push the performance limits of converters. As part of your work, you will select the most promising control concept and develop a tailored control strategy based on an existing simulation model of our next-generation charger converter topologies. You will examine advanced closed-loop control methods suitable for highly nonlinear power electronic systems, focusing on model predictive control (MPC), neural network-based control architectures, and reinforcement learning (RL) approaches for power converters. Furthermore, you will implement the developed control algorithm in a simulation environment (e.g., MATLAB/Simulink, Python, or PLECS). Furthermore, you will validate and benchmark the control concept under realistic operating conditions, evaluating its transient response, efficiency, and stability in comparison to established reference controllers. You will analyze and compare the identified control strategies with regard to computational effort, dynamics, robustness to parameter changes, and suitability for real-time implementation. Finally, you will create the documentation and present the results.

• Education: Master's degree in electrical engineering, control engineering, mechatronics, cybernetics, computer science or a comparable qualification
• Experience and skills: Sound knowledge of control systems (state space, nonlinear control or predictive modern control methods); confident use of simulation tools such as MATLAB/Simulink and/or Python with machine learning frameworks such as PyTorch; knowledge of power electronics (AC/DC and DC/DC converters) is advantageous.
• Personality and work style: You are a goal-oriented and motivated person with a solution-oriented mindset.
• Working model: We offer you the opportunity to work in a hybrid working model.
• Enthusiasm: High motivation to work on future-oriented research at the interface of power electronics and intelligent control systems (machine learning, reinforcement learning).
• Languages: Fluent English or German

As long as this position is advertised in our job portal, we are looking for suitable candidates.

For data protection reasons, applications are only accepted via our application portal. We regret that we cannot consider applications submitted by email or post.