The design and development of innovative safety contracts will be your core focus, establishing formal runtime interfaces to ensure safe interaction between decision-making and control in autonomous systems.
You will precisely define assumptions, guarantees, and adaptation rules for modular autonomy stacks to secure system integrity across different abstraction levels.
More over, you will utilize advanced methods of optimal control, sequential decision-making under uncertainty, and reinforcement learning to develop robust, high-performance solutions.
Using machine learning, you will derive feasibility margins, uncertainties, and the safety contracts themselves from real-world data.
Translating research findings into concrete applications is a key goal, particularly in the domain of vehicle motion control and autonomous driving, to safely bridge the gap between high-level decisions and physical reality.
Last but not least, you will contribute significantly to making learning-enabled autonomy modular by design, guaranteeing safety by construction, and enabling verification at runtime.
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