Research Assistant / Research Associate (Software, AI & Autonomous Systems)
Required Qualifications
Bachelor's or Master's degree in Robotics, Computer Science, Artificial Intelligence, Electrical Engineering, Computer Engineering, or related disciplines.
Key Responsibilities
- Develop AI, machine learning, optimisation, and decision-support algorithms.
- Design perception and sensor fusion frameworks using cameras, LiDAR, depth sensors, and other sensing technologies.
- Develop object detection, segmentation, tracking, and pose estimation algorithms for material handling applications.
- Develop navigation, localisation, path planning, and collision avoidance algorithms.
- Build digital twin environments and physics-based simulation models.
- Develop autonomous material handling and stacking logic for irregular and deformable materials.
- Implement system monitoring, safety supervision, and fault detection algorithms.
- Conduct simulation studies, performance evaluations, and algorithm validation.
- Develop and apply Generative AI, Embodied AI, and Physical AI frameworks
- Support software integration with robotic hardware and control systems.
- Work closely with stakeholders, end users, industry partners and multidisciplinary research teams.
Preferred Skills
- Python, C++, ROS/ROS2.
- Computer Vision (OpenCV, Deep Learning).
- AI/ML frameworks (PyTorch, TensorFlow).
- Digital Twin and simulation tools (Isaac Sim, Gazebo, MuJoCo, CoppeliaSim), proof of concept testing.
- Autonomous navigation and motion planning.
- Sensor fusion and perception systems.
- Multi-objective optimisation and AI-based decision-making.
- Experience with robotic manipulation and autonomous systems is advantageous.
- Experience in Generative AI, Embodied AI, Physical AI, Foundation Models, Vision-Language-Action (VLA) Models, Reinforcement Learning, and AI-driven robotic autonomy is advantageous.
The successful candidate will contribute to the development of intelligent software, AI algorithms, perception systems, digital twin simulations, optimisation frameworks, and autonomous decision-making capabilities for robotic systems involved in the handling, moving, and stacking of various materials with different shapes, sizes, and weights within confined spaces.
