Research Fellow / Engineer – Automated Loading & Unloading System

About the role

The Singapore Institute of Technology seeks a Research Fellow/Engineer to lead applied AI research on an automated loading and unloading system for aircraft cargo Unit Load Devices (ULDs). The role involves developing and validating a multimodal perception pipeline for ULD alignment, cargo‑loading system lock verification, and safety checks within a simulated lower‑deck cargo compartment.

Key responsibilities

• Collaborate with the Principal Investigator and project team to meet milestones, technical performance measures and deliverables.
• Define perception scenarios, collect and annotate RGB‑D datasets, and create synthetic‑data workflows for simulation.
• Design, train and evaluate computer‑vision models for ULD alignment and lock‑state detection, integrating depth sensing and sensor‑fusion techniques.
• Deploy and benchmark the perception pipeline in laboratory and mock‑up environments, supporting integration with robotic manipulation and navigation.
• Document datasets, models and results; contribute to technical reports, publications and invention disclosures.

Required profile

• Bachelor’s degree in Computer Science, Engineering, Robotics, AI or related field with at least three years of relevant experience, or a Master’s/PhD.
• Strong research record demonstrated by peer‑reviewed publications, patents or open‑source contributions.
• Experience working with physical robotic systems or safety‑critical aviation/logistics applications is advantageous.

Required skills

• Python programming.
• Deep‑learning frameworks such as PyTorch or TensorFlow.
• Computer‑vision techniques: object detection, segmentation, pose/alignment estimation.
• RGB‑D cameras, depth sensors, and multimodal sensor‑fusion.
• Robotics middleware (ROS/ROS 2) and simulation platforms (e.g., NVIDIA Isaac Sim).