Job Description

The Department of Civil and Environmental Engineering at the National University of Singapore (NUS) invites applications for one (1) Research Engineer position in the Constructive Machines Lab. The lab builds robotic systems that autonomously build structures and studies the two-way relationship between how a structure is designed and how it is built. Its current focus is a team of mobile manipulators that transport and assemble scaffolding systems, temporarily support unstable partial assemblies, and progressively turn the structure-in-progress into navigable space for themselves. The lab investigates both conventional planning pipelines and recent learning-based approaches, and the candidate will have the opportunity to work across both.

The Research Engineer is expected to:
• Develop and evaluate task and motion planning methods for assembly in environments whose reachable configuration space expands as construction proceeds.
• Formulate and solve assembly sequencing problems under structural stability constraints, including when temporary supports are required and which robot provides them.
• Develop multi-robot task allocation and scheduling methods that minimise total construction time for a given number of robots.
• Implement and benchmark planning pipelines in physics simulation and quantify achievable construction throughput across scenarios.

Job Requirements

•    At least a Bachelor’s degree in computer science, Robotics, Mechanical Engineering, Electrical Engineering, or a related field. 
•    A Master's degree will be preferred.
•    Solid grounding in robot motion planning, including sampling-based planners, collision checking, inverse kinematics, and trajectory optimisation.
•    Familiarity with task planning or combinatorial optimisation, such as symbolic planning, constraint programming, mixed-integer programming, or scheduling.
•    Strong programming skills in Python and/or C++, with clean, well-documented, and reproducible code.
•    Experience with physics simulation and robotics simulation platforms such as Isaac Sim/Lab, MuJoCo, PyBullet, Drake, or Gazebo.
•    Experience with learning-based planning or decision-making, for example reinforcement learning or learned search heuristics with PyTorch, is a plus.
•    Prior research experience in task and motion planning, assembly planning, or multi-agent coordination is highly preferred, including experience gained during a Master's or PhD programme.
•    Ability to work both independently and collaboratively within a multidisciplinary research team.
•    Strong written and spoken communication skills.
•    Open to fixed-term contract.