NATIONAL UNIVERSITY OF SINGAPORE Hiring! Full Time Research Assistant (SSI) in West Region (Singapore), Earn up to SGD 4,750 - Ricebowl

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

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We regret that only shortlisted candidates will be notified.

Job Description

The Research Assistant will develop the core components of the Data Engine Minimum Viable Product (DEM), an end-to-end system for collecting, curating and leveraging large-scale robot manipulation data for training generalist robot policies.

Specific duties include:

•                 Develop automated data quality assessment methods, including uncertainty quantification and out-of-distribution detection, to identify and filter low-quality or unsafe demonstrations.

•                 Build and maintain teleoperation and data-collection rigs for real robot platforms, including motion retargeting, inverse kinematics and bimanual/whole-body control.

•                 Train, fine-tune and benchmark vision-language-action (VLA) and imitation learning policies on curated datasets; run systematic studies on data scale, mixture and augmentation. Optimise policy inference for real-time deployment on physical robots.

•                 Maintain a well-documented, reproducible research codebase and support dataset and software releases.

•                 Contribute to publications, technical reports and progress reports to the funding partner; present findings at project and group meetings.

•                 Mentor student assistants and interns, and collaborate with project partners and other lab members.

Qualifications and Requirements

• Master's degree in Electrical/Electronic Engineering, Computer Science, Mechanical Engineering, Robotics or a related discipline.
• Strong programming ability in Python and C++, with hands-on experience in ROS/ROS2.
• Practical experience with real robot manipulators, including teleoperation, retargeting, inverse kinematics or optimal/adaptive control.
• Experience training deep learning models in PyTorch, with working knowledge of imitation learning / behaviour cloning and vision-language-action or large multimodal models.
• Experience building large-scale data pipelines — dataset curation, standard robot data formats, versioning and quality control — is an advantage.
• Track record of peer-reviewed publications at robotics or machine learning venues (e.g. CoRL, ICRA, IROS, RSS, NeurIPS) preferred.
• Good written and spoken English; able to work independently as well as in a collaborative, multi-disciplinary team.