Research Fellow (Multi-Agent RL for Autonomous Drone Swarm)

Nanyang Technological University; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

Nanyang Technological University (NTU Singapore) invites applications for a Research Fellow in Multi-Agent RL for Autonomous Drone Swarm. The role focuses on learning-based coordination of drone teams, decentralized task assignment, and robust perception-driven decision making in urban and cluttered environments.

You will work with PhD students and engineers, publish in top venues, and contribute to project demonstrations and reports within tight timelines.

Qualifications

• PhD in Robotics, Aerospace, Mechanical, Electrical & Electronic Engineering, CS, AI or closely related discipline.
• Strong research background in multi-agent RL, multi-robot systems, autonomous systems, or learning-based navigation.
• A strong publication record in relevant journals or conferences would be an advantage.

Responsibilities

• Develop learning-based frameworks for cooperative multi-agent robotic systems in complex environments.
• Formulate multi-agent decision-making problems including state/action representation, reward design, task allocation, and decentralized policy learning.
• Develop RL and MARL algorithms for autonomous coordination and target-following under uncertainty, partial observability, and dynamic conditions.
• Develop perception-aware decision-making methods for autonomous agents in changing targets/environments.
• Integrate perception, decision-making, and control in a simulation-based validation framework.
• Design and conduct simulation experiments to evaluate performance, robustness, and scalability.
• Collaborate with PhD students, researchers, and engineers to support system integration, testing, and demonstration.
• Prepare technical reports, publications, presentations, and project deliverables.

Skills

Multi-agent RL

Robotics

Python

PyTorch

TensorFlow

Reinforcement Learning

Graph Neural Networks

Computer Vision

Education

PhD in Robotics/AE/ME/EEE/CS/AI

Tools

Unity

ROS/ROS2

Gazebo

AirSim

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 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 https://www.ntu.edu.sg/mae/research. We are looking for a Research Fellow in Multi-Agent RL for Autonomous Drone Swarm to develop learning-based algorithms for cooperative target tracking in complex environments.

The role will focus on multi-agent reinforcement learning, decentralized target assignment, occlusion-aware decision-making, and communication-resilient coordination for autonomous drone teams operating in urban and cluttered environments.

Key Responsibilities:

• Develop learning-based frameworks for cooperative multi-agent robotic systems operating in complex environments.
• Formulate multi-agent decision-making problems, including state and action representation, reward design, task allocation, and decentralized policy learning.
• Develop reinforcement learning and multi-agent reinforcement learning algorithms for autonomous coordination and target-following tasks under uncertainty, partial observability, and dynamic environmental conditions.
• Develop perception-aware decision-making methods that enable autonomous agents to respond to changing target and environment conditions.
• Integrate perception, decision-making, and control modules within a simulation-based validation framework.
• Design and conduct simulation experiments to evaluate system performance, robustness, scalability, and generalization.
• Work with PhD students, research engineers, and collaborators to support system integration, testing, and demonstration.
• Prepare technical reports, research publications, presentations, and project deliverables.

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.
• 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 with reinforcement learning and multi-agent reinforcement learning algorithms.
• Familiarity with simulation environments for robotics or autonomous systems, such as Unity, ROS/ROS2, Gazebo, AirSim or equivalent platforms.
• Knowledge of multi-agent coordination, decentralized control, target assignment, or swarm robotics.
• Familiarity with graph neural networks, attention mechanisms would be advantageous.
• Experience with computer vision, sensor fusion, or multi-object tracking would be beneficial.
• Experience Experience in developing and training reinforcement learning or multi-agent learning policies in simulation.
• Experience in multi-agent robotic systems, drone swarms, or autonomous vehicle coordination.
• Prior experience with real-world robotic or UAV experiments would be an advantage, but is not mandatory.
• Competencies Ability to design robust learning algorithms under uncertainty and communication constraints.
• 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 A research-intensive public university, Nanyang Technological University, Singapore (NTU Singapore) has 33,000 undergraduate and postgraduate students in the Engineering, Business, Science, Humanities, Arts, & Social Sciences, and Graduate colleges. It also has a medical school, the Lee Kong Chian School of Medicine, established jointly with Imperial College London. NTU is also home to world-class autonomous institutes – the National Institute of Education, S Rajaratnam School of International Studies, Earth Observatory of Singapore, and Singapore Centre for Environmental Life Sciences Engineering – and various leading research centres such as the Nanyang Environment & Water Research Institute (NEWRI) and Energy Research Institute @ NTU (ERI@N). Ranked amongst the world’s top universities by QS, NTU has also been named the world’s top young university for the past seven years. The University’s main campus is frequently listed among the Top 15 most beautiful university campuses in the world and has 57 Green Mark-certified (equivalent to LEED-certified) buildings, of which 95% are certified Green Mark Platinum. Apart from its main campus, NTU also has a campus in Novena, Singapore’s healthcare district. Under the NTU Smart Campus vision, the University harnesses the power of digital technology and tech-enabled solutions to support better learning and living experiences, the discovery of new knowledge, and the sustainability of resources.

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