M.Sc. Thesis on Adaptive Data Driven Control for Wide Area Power System Oscillation Damping (m/w/d)

The opportunity

Join our System and Automation team at the Swiss research center and contribute to pioneering work at the intersection of Machine Learning, Control, and Power Systems. You will work in a dynamic, multidisciplinary environment that values collaboration, creativity, and scientific curiosity. In this six-month MSc thesis project, you will explore innovative ML-driven controller adaptation methods that support a more stable and sustainable energy future. You will receive dedicated mentoring, gain hands-on experience with in-house simulations, and develop solutions that can influence future industrial applications. This is a great opportunity to strengthen your technical profile while advancing real-world impact.

How you'll make an impact

• Conduct literature research on adaptive control and data-driven controller adaptation
• Develop ML-based methods to adapt ARX-based data-driven controllers
• Implement and evaluate different approaches using in-house simulations
• Benchmark results against existing internal baselines
• Explore, test, and refine model variations through ablation studies
• Deliver high-quality technical documentation and scientific reporting
• Share insights within the team and propose creative improvements
• Drive tasks proactively under supportive supervision

• Enrolled in an MSc in Applied Mathematics, Electrical Engineering, Control, Automation, or a related field
• Knowledge in at least one area: Machine Learning, Dynamic Systems and Control, or Power Systems
• Skilled in Python
• Strong analytical mindset and initiative
• Excellent communication skills and a collaborative attitude
• Fluent in English, spoken and written