Research Assistant/Associate

Research Assistant / Research Associate (Software, AI & Autonomous Systems)

Required Qualifications

Bachelor's or Master's degree in Robotics, Computer Science, Artificial Intelligence, Electrical, Computer Engineering, or related disciplines.

Key Responsibilities

• Systematically define user and system requirements, develop system architecture, and conduct feasibility assessments.
• Design perception and sensor fusion frameworks using cameras, LiDAR, depth sensors, and other sensing technologies.
• Develop object detection, segmentation, tracking, and pose estimation algorithms.
• Develop navigation, localisation, path planning, and collision avoidance algorithms.
• Develop AI, machine learning, optimisation, and decision-support algorithms.
• Build digital twin environments, physics-based modelling, concept evaluation frameworks, and technical risk assessments.
• 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.
• Collaborate closely with stakeholders, end users, industry partners, and multidisciplinary research teams to support system integration and project delivery.

Preferred Skills

• Python, C++, ROS/ROS2.
• Computer Vision (OpenCV, Deep Learning).
• AI/ML frameworks (PyTorch, TensorFlow).
• Autonomous navigation and motion planning.
• Sensor fusion and perception systems.
• Experience with robot simulation, digital twin development, and physics-based modelling tools (NVIDIA Isaac Sim, Gazebo, MuJoCo, CoppeliaSim, Modelica), including AI-assisted design exploration and virtual validation.
• Multi-objective optimisation and AI-based decision-making.
• Experience with autonomous mobile robots, manipulation and autonomous systems.
• Experience in Generative AI, Embodied AI, Physical AI, Foundation Models, Vision-Language-Action (VLA) Models, Reinforcement Learning, and AI-driven robotic autonomy.

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.