Research Fellow (Multi-Agent Path Planning for Autonomous Drone Operations)

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 view . We are looking for a Research Fellow in Multi-Agent Path Planning for Autonomous Drone Operations to develop learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor crash predictions with vessel protected volume estimates to identify safety-buffer overlaps, developing multi-agent path-planning, and machine-learning methods for corridor allocation, airspace capacity optimisation, and defining safe air corridors and operational capacity limits under varying maritime traffic conditions. Key Responsibilities: Develop multi-agent path-planning frameworks for safe and efficient multirotor operations in maritime environments. Formulate multi-agent navigation problems, including state and action representation, safety constraints, vessel-protected volumes, route allocation, and trajectory optimisation. Develop model predictive control and multi-agent model predictive control algorithms for path planning under dynamic maritime conditions. Develop capacity optimisation algorithms 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 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 PhD degree in Robotics, Aerospace Engineering, Mechanical Engineering, Electrical and Electronic Engineering, Computer Science, Artificial Intelligence, or a closely related discipline. Strong research background in multi-agent reinforcement learning, multi-robot systems, autonomous systems, or learning-based navigation. A strong publication record in relevant journals or conferences would be an advantage. Soft skills Strong communication and problem-solving skills. Strong sense of ownership, responsibility, and initiative. Ability to mentor junior researchers, PhD students, or research engineers and collaborate with industry partners, and project stakeholders. Willingness to support project reporting and milestone reviews. Hard skills Strong programming skills in Python and deep learning frameworks such as PyTorch or TensorFlow. Experience in autonomous navigation, path planning, trajectory optimisation, conflict detection, or collision avoidance. Experience in model predictive control and multi-agent model predictive control algorithms. 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 traffic-aware navigation would be advantageous. Familiarity with geospatial data, maritime traffic data, Automatic Identification System data, or airspace-capacity modelling would be beneficial. Experience Experience in developing learning-based model predictive control algorithms in simulation. Experience in multi-agent robotic systems, drone swarms, or autonomous decision making. Experience in integrating prediction, planning, decision-making, and control modules within a simulation or experimental framework. Experience with control algorithms for embodied AI systems is desirable. Competencies Ability to formulate and solve navigation and optimisation problems under uncertainty, dynamic constraints, and changing operational conditions. Ability to work across AI, robotics, control, and UAV autonomy domains. Ability to deliver research outcomes within project timelines and contribute to high-quality publications We regret to inform that only shortlisted candidates will be notified. Hiring Institution: NTU