Research Fellow

Job Req ID: 2505

Employee Category: Research

Department: EPD Pillar

Research Fellow (Software, AI & Autonomous Systems)

Required QualificationsPhD in Robotics, Computer Science, Artificial Intelligence, Electrical, Computer Engineering, or related disciplines.

Key ResponsibilitiesSystematically 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 frameworksSupport 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 SkillsPython, 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.