Research Fellow, UAV Health Monitoring
- Develop intelligent health monitoring and fault prognosis system for UAVs.
- Research on related topics and publish high-quality academic papers.
- A PhD degree in UAV health monitoring and fault prognosis field.
- Knowledge in machine learning, particle filtering, statistical inference, data analysis.
- Strong mathematical background is preferred.
- Experience in MATLAB, Python, C++ and LaTex.
- Excellent command of English.
- Knowledge of aerodynamics is a plus.
Location: Kent Ridge Campus
Department: Mechanical Engineering