Master Thesis: Autonomous Agentic Control Design

About the role

This master thesis focuses on designing autonomous meta‑agents that can automate the end‑to‑end synthesis of advanced control systems. You will work at the intersection of generative AI and safety‑critical control engineering, creating a verifiable framework that translates modern control theory into automated design steps.

Key responsibilities

• Develop a reasoning framework that guides an autonomous agent in selecting and synthesizing control architectures.
• Integrate nonlinear control methods (exact linearization, backstepping, sliding mode) with optimization‑based approaches (model predictive control, reinforcement learning).
• Build a closed‑loop simulation environment in Python where the agent iteratively generates code, runs simulations, and validates safety and performance.
• Document the methodology and results for academic and internal Bosch review.

Required profile

• Enrolled in a Master program in Cybernetics, Engineering, Mathematics, Computer Science or a related field.
• Strong analytical thinking and an autonomous, systematic working style.
• Very good command of English.

Required skills

• Python programming.
• Control engineering fundamentals.
• Machine learning, including reinforcement learning.
• Familiarity with model predictive control and nonlinear control techniques.

What we offer

• Opportunity to work on cutting‑edge AI‑driven control research within a global technology leader.
• Partially mobile working arrangement (hybrid).
• Access to Bosch’s expertise, tools, and physical benchmark systems.