Research Fellow (Multi-UAVs Path Planning)
Research Fellow (Multi-UAVs Path Planning)
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Job Description
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 nations long-term development 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.
Developing advanced path planning search and exploration algorithms for multi-UAVs systems in unknown and complex 3D environments.
Designing efficient obstacle avoidance strategies to ensure collision-free navigation in dense settings.
Implementing and validating algorithms in both virtual and real-world scenarios to optimize performance in indoor and outdoor environments.
Mandatory Requirements:
Ph.D. in Robotics Computer Science Electrical Engineering or related fields with a focus on Path Planning Multi-Robot Systems or Autonomous Navigation.
Strong research background in path planning motion planning and multi-robots coordination in complex environments.
Proficiency in Python and C with extensive experience in ROS/ROS2 for robotic development.
Familiarity with popular path planning algorithms such as RRT A* and optimization-based methods.
Hands-on experience with simulation environments such as Gazebo and Unity3D.
Excellent verbal and written communication skills in English for research work
Preferred Requirements:
Experience with deep learning and reinforcement learning algorithms in robotic decision.
Familiar with UAV kinematics and dynamics.
We regret to inform that only shortlisted candidates will be notified.
Hiring Institution: NTUEmployment Type
Full-Time
