Research Fellow

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.

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We welcome applications for a Research Fellow position in Multi-UAVs Path Planning at the School of Mechanical and Aerospace Engineering at Nanyang Technological University, Singapore.

This is a full-time appointment funded for about a 1 year (depends on hiring date).

The selected candidate will join a group equipped with state-of-the-art facilities to work on the following:

• 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.