Job Description

This position involves working on a project related to the coordination of large teams of autonomous robots (e.g., UAVs) in mixed cooperative-competitive environments. The Research Assistant will support the development, simulation, testing, and implementation of collaborative control algorithms for multi-agent robotic systems, under the guidance of the PI and senior researchers. The work will first focus on simulation studies and may later involve hardware experiments with project partners. The role provides an opportunity to work with the PI’s team at the National University of Singapore, as well as industrial, government, and overseas collaborators. The successful candidate is expected to be motivated, responsible, and willing to learn, with a Master’s-level background in robotics, control, computer science, electrical engineering, mechanical engineering, or related disciplines. Prior exposure to multi-agent systems, robotic control, reinforcement learning, or autonomous aerial vehicles would be an advantage.

The main research tasks for the project include, but are not limited to:

• Assisting in the development and evaluation of conventional and learning-based controllers for collaborative control of multi-agent robotic systems in mixed cooperative-competitive environments.
• Implementing and testing control algorithms in simulation environments, with possible support for hardware experiments on robotic or UAV platforms.

Qualifications

• Good programming skills in Python.
• Basic experience with PyTorch, deep learning, reinforcement learning, or related machine learning tools.
• Basic understanding of robotics, control systems, machine learning, or multi-agent systems.
• Experience with robotic simulation environments or aerial simulators would be an advantage.
• Prior exposure to UAVs, autonomous robots, or hardware experiments would be beneficial but is not required.• Ability to read research papers and summarize key technical ideas.
• Good written and spoken communication skills.
• Ability to work independently on assigned tasks while collaborating effectively within a research team.