Research Associate (Embodied AI)

We are part of EEE at NTU. One of our primary research focuses is on embodied AI that involves VLA adaptation, data collection, benchmarking, and beyond.

School of Electrical and Electronic Engineering is one of the founding Schools of the Nanyang Technological University. Built on a culture of excellence, the School is renowned for its high academic standards and research. With over 3,000 undergraduates students and 2,000 graduate students it is one of the largest EEE schools in the world and ranks 4th in the field of Electrical & Electronic Engineering in the 2025 QS World University Rankings by Subjects.

Today, the School has become one of the world’s largest engineering schools that nurtures competent engineers and researchers. Each year, the School graduates over a thousand students who are ready to take on great ambitions and challenges.

For more details, please view: https://www.ntu.edu.sg/eee

The candidate is required to carry out research in embodied AI, especially focused on vision-language-action (VLA) models. This includes problem and solution formulation, VLA model training, evaluation, data collection, and related tasks. One key requirement is meeting the strict deadlines in terms of deliverables: model checkpoint, code, dataset, documentation, report, etc. Physical presence will often be required on site. Familiarity with robotic arms, end-effectors, teleoperation, and related concepts is a bonus. Likewise, familiarity with robotic simulation like Mujoco would be advantageous.

• →Problem and solution formulation including coding
• →Stay abreast of latest developments in the area
• →Vision-language-model training
• →Model evaluation
• →Data collection pipeline development
• →Data collection
• →Documentation

• Masters qualification degree in Computer Science, Mathematics, Physics, Electrical Engineering, Mechanical Engineering, or related field
• Knowledgeable in machine learning, deep learning, python, PyTorch, and programming in general.
• Knowledgeable in transformers and diffusion and flow-based models.
• Knowledgeable in reinforcement learning.
• Knowledgeable in large language models, vision language models, vision-language-action models, and related concepts.
• Capable of coming up with interesting approaches to solve problems.
• Capable of quickly adapting to new domains/subdomains.
• Good communication skills.
• Skilled with agentic coding.

We regret to inform you that only shortlisted candidates will be notified.