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Fresher
SGD 4,500 - 9,000 per month
  • Posted 4 days ago
  • Be among the first 10 applicants

Job Description

Research Assistant(Embodied AI & Robotics System Integration)

About the Job:
You will conduct hands-on research to develop and deploy learning-based intelligence on real robots connected over wireless networks. You will set upand integrate robot platforms in ROS 2, collect demonstration data, train andfine-tune robot learning models such as vision-language-action (VLA) models,and deploy them on physical robots. Working with wireless engineers andindustry partners, you will study how robot intelligence performs in connected,real-world settings. You will contribute to research publications anddemonstrations. Fresh graduates and early-career engineers with strong hands-onproject experience are encouraged to apply.

Who We're Looking For:

  • Bachelor's or Master's degree in Robotics, Computer Science, Electrical Engineering, Mechatronics, or related field.
  • Hands-on experience building or programming real robots, e.g., through capstone projects, robotics competitions, internships, or open-source projects, demonstrated through code repositories or demo videos.
  • Proficiency in Python experience with PyTorch or another deep learning framework.
  • Experience with ROS/ROS 2 and Linux.
  • Familiarity with, or strong interest in, robot learning such as imitation learning, VLA models, or world models.
  • Hands-on experience training or deploying learned policies on robots (e.g., with LeRobot), including teleoperation data collection, is highly valued.
  • Experience with robot simulators (e.g., Isaac Sim, MuJoCo, Gazebo), embedded GPUs (e.g., NVIDIA Jetson), or C++ is a plus.
  • Experience with robot arms, mobile robots, or humanoid robots, and interest in wireless or networked robotics, is a plus.
  • Eagerness to learn, problem-solving skills, and the ability to work in a team.

More Info

Job Type:
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Key Skills

ROS 2

world models

Isaac Sim

MuJoCo

Gazebo

NVIDIA Jetson

imitation learning

LeRobot

VLA models

robot learning

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