Research Engineer (Autonomous Drone Navigation And Path Planning)

The School of Mechanical& Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE prides itself in its excellent research capabilities in areas including advanced manufacturing, aerospace, biomedical, energy, industrial engineering, maritime engineering, robotics, etc. The school is equipped with state-of-the-art research infrastructure, housing a comprehensive range of cluster laboratories, test bedding facilities, research centres/institutes and corporate laboratories. Cutting-edge research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in developing new competencies to support the growth and competitiveness of our engineering sector in the global landscape. MAE has grown to be leader in Engineering Research, ranking amongst the top engineering schools in the world. For more details, please viewhttps://www.ntu.edu.sg/mae/research. We are looking for a Research Engineer in Autonomous Drone Navigation for Maritime Environments to develop safe and efficient navigation algorithms for multirotor operations around vessels. The role will focus on integrating drone airspace-usage predictions with Vessel Protected Volume models, developing optimisation and machine-learning methods for conflict detection, separation assurance, and collision-free flight-path planning, and estimating the maximum operational capacity under varying maritime traffic and environmental conditions. Key Responsibilities: Develop autonomous drone navigation frameworks for safe and efficient multirotor operations in maritime environments. Formulate navigation and separation-assurance problems, including state and action representation, safety constraints, vessel-protected volumes, route allocation, and trajectory optimisation. Develop optimisation and machine-learning algorithms for conflict detection, collision avoidance, flight-path planning, and rerouting under uncertain and dynamic maritime traffic conditions. Develop perception- and prediction-aware navigation methods that enable drones to respond to vessel movements, environmental disturbances, and changes in operational constraints. Integrate drone airspace-usage prediction, Vessel Protected Volume estimation, navigation, decision-making, and control modules within a simulation-based validation framework. Develop methods to estimate the maximum safe operational capacity for drone operations under different vessel traffic, airspace, and environmental scenarios. Design and conduct simulation experiments, sensitivity analyses, and validation studies to evaluate navigation safety, efficiency, robustness, scalability, and generalisation. Work with PhD students, research engineers, vessel operators, drone operators, and project collaborators to support system integration, testing, validation, and demonstration. Prepare technical reports, research publications, presentations, project deliverables, and documentation for stakeholder and expert review. Job Requirements: Education qualifications Bachelor’s or Master’s degree in Aerospace Engineering, Mechanical Engineering, Electrical and Electronic Engineering, Robotics, Computer Science, Artificial Intelligence, or a closely related discipline. Strong academic or project background in autonomous navigation, path planning, trajectory optimisation, robotics, or unmanned aerial systems. Research or project experience in maritime autonomy, airspace management, or autonomous vehicle coordination would be advantageous. Soft skills Strong communication and problem-solving skills. Strong sense of ownership, responsibility, and initiative. Ability to work effectively with researchers, engineers, students, industry partners, and project stakeholders. Willingness to support project reporting and milestone reviews. Hard skills Strong programming skills in Python, C++, or MATLAB. Experience in autonomous navigation, path planning, trajectory optimisation, conflict detection, or collision avoidance. Familiarity with optimisation techniques, machine-learning methods, and data-driven modelling. Familiarity with robotics and autonomous-system simulation environments such as ROS/ROS2, Gazebo, AirSim, Unity, or equivalent platforms. Knowledge of vessel-motion prediction, dynamic obstacle avoidance, separation assurance, or…