AI Research Fellow: Automated ULD Loading & Safety Systems

Singapore Institute of Technology; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

Singapore Institute of Technology (SIT) seeks a Research Fellow/Research Engineer to develop an AI-enabled multimodal perception pipeline for Automated Loading and Unloading System of ULDs. You will design, train and evaluate CV and multimodal models, integrate depth sensing and sensor fusion, and validate the pipeline in a LDCC mock-up environment.

Responsibilities include data collection, model evaluation, and coordination with PI/Co-PI.

Qualifications

• Bachelor's degree in CS/CE/EE/Robotics/AI or related field.
• Strong research in deep learning and computer vision with hands-on experience in detection, segmentation, pose/alignment estimation, depth perception and 3D vision.
• Proficiency in Python and frameworks such as PyTorch or TensorFlow with practical experience building perception pipelines.

Responsibilities

• Participate in and manage research activities with PI/Co-PI to meet milestones and deliverables.
• Define perception scenarios, safety-check criteria and lighting/occlusion for ULD positioning.
• Collect and label RGB/depth data, curate datasets for ULD alignment and CLS lock verification.
• Develop synthetic data workflows for LDCC/ULD/CLS geometries using existing assets.

Skills

Deep learning

Computer vision

Object detection

Segmentation

Pose estimation

Depth perception

3D vision

Sensor fusion

Python

PyTorch

TensorFlow

ROS/ROS 2

NVIDIA Isaac Sim

Education

Bachelor’s degree in CS/CE/EE/Robotics/AI or related field

Master’s or PhD in related field

Tools

Python

PyTorch

TensorFlow

ROS/ROS 2

NVIDIA Isaac Sim

Job description

Singapore Institute of Technology (SIT) seeks a Research Fellow/Research Engineer to develop an AI-enabled multimodal perception pipeline for Automated Loading and Unloading System of ULDs. You will design, train and evaluate CV and multimodal models, integrate depth sensing and sensor fusion, and validate the pipeline in a LDCC mock-up environment.

Responsibilities include data collection, model evaluation, and coordination with PI/Co-PI.