- Design and develop data-driven and machine-learned approaches to vehicle control problems.
- Develop learned models of vehicle behavior and dynamics and integrate them into the closed-loop simulation.
- Apply machine learning to improve how the controller adapts across vehicles and operating conditions.
- Own problems end to end, from conceptualization and offline experimentation through simulation and on-vehicle validation.
- Build the data pipelines, evaluation metrics, and tooling needed to measure whether a learned approach outperforms the classical baseline.
- Participate in technical and architecture discussions to define how learning and classical control coexist in a safety-critical stack.
PythonMachine LearningPyTorch+1 more