Master Thesis Autonomous Agentic Control Design - Translating System-Theoretic Properties into Self-Improving Synthesis Loops
Core
Designing autonomous meta-agents to automate end-to-end control system design by translating system-theoretic properties into self-improving synthesis loops.
Role type
Master Thesis Researcher (Autonomous Control Systems)
Builds
Closed-loop simulation framework where agents interact with physical benchmarks to synthesize and verify control architectures.
Domain
Cybernetics, Control Engineering, Machine Learning
Deliverable
production ML models
Required skills
control engineering, machine learning, Python, agentic AI, nonlinear control methods, optimization-based control
Preferred skills
exact linearization, backstepping, sliding mode, model predictive control, reinforcement learning
Technologies
Python
Responsibilities
Design a reasoning framework for agent-based controller selection and synthesis; build a simulation framework for iterative code generation and safety verification; analyze critical requirements like constraint satisfaction and safety boundaries.
Seniority
Master Thesis (6 months)
