About the role
Research Fellow (Multi-Agent RL for Autonomous Drone Swarm) is a active engineering role at ntu in NTU Main Campus, Singapore. Open the role to review the official description and apply on the company site.
CompanyOnsite
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.
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.