Research Engineer (Hierarchical Learning for Multi-ASVs)

National University of Singapore; Singapore; On-site; SGD 100,000 - 160,000; Full time

Job summary

National University of Singapore is seeking a Research Engineer to work on hierarchical learning for a team of autonomous surface vehicles. The role involves developing learning-based controllers, implementing them in simulation and on hardware, and collaborating with partners on a water-based manipulation project.

The candidate should have a strong background in robotics, control, and computer science with emphasis on reinforcement learning, imitation learning, and generative motion modeling,

Qualifications

• Background in robotics, control engineering, or related field with a working foundation in classical control theory alongside modern learning-based methods.
• Excellent coding skills in Python with PyTorch, including reinforcement learning, imitation learning, and flow matching/diffusion-based generative models.
• Experience designing hierarchical control architectures that combine high-level RL policies with generative motion models and low-level tracking controllers.
• Familiarity with various conditioning architectures for generative sequence models.
• Hands-on experience with robotics simulation platforms (e.g. Isaac Lab, MuJoCo) and sim-to-real transfer for deploying learned policies on hardware.
• Good writing/spoken communication skills for writing research reports and presentations

Responsibilities

• Developing robust conventional and learning-based controllers for collaborative manipulation by a team of autonomous surface vehicles.
• Designing and implementing controllers in simulation and then on hardware with our partner

Skills

Python

PyTorch

Reinforcement learning

Imitation learning

Generative models

Flow matching

Diffusion models

Tools

Isaac Lab

MuJoCo

Job description

Job Title: Research Engineer (Hierarchical Learning for Multi-ASVs)

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Mechanical Engineering

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 29/06/2026

Job Description

This position involves working on a project related to the planning and control of a team of autonomous surface vehicles, for collaborative manipulation tasks on the surface of water. The objective of this position includes contributing to model development, solution, and implementation of such collaborative controllers on actual platforms, first in simulation and then on hardware. The role involves the opportunity to work with the PI's team within the National University of Singapore, as well as a large industrial partner. The successful candidate will be self-motivated with an outstanding track record in robotics, control, computer science, or related disciplines, with a special focus on reinforcement learning, imitation learning, and generative motion modelling. The main research tasks for the project include but will not be limited to:

• Developing robust conventional and learning-based controllers for collaborative manipulation by a team of autonomous surface vehicles.
• Designing and implementing controllers in simulation and then on hardware with our partner

Qualifications

• Background in robotics, control engineering, or a related field, with a working foundation in classical control theory alongside modern learning-based methods.
• Excellent coding skills in Python with PyTorch, including reinforcement learning, imitation learning, and flow matching/diffusion-based generative models.
• Experience designing hierarchical control architectures that combine high-level RL policies with generative motion models and low-level tracking controllers.
• Familiarity with various conditioning architectures for generative sequence models.
• Hands-on experience with robotics simulation platforms (e.g. Isaac Lab, MuJoCo) and sim-to-real transfer for deploying learned policies on hardware.
• Good writing/spoken communication skills for the purpose of writing research reports and presentations

Req ID: 33552