Skills

- Hands-on training experience in Robotics o Hands-on training experience in robot learning techniques, such as reinforcement learning, imitation learning as well as classical control methods o Understanding of Robot kinematics, dynamics and sensors o Familiarity with control as PID, model predictive control (MPC), and whole-body control.
- Skills in algorithmic problem solving and software proficiency for Robotics (ROS/ROS2, C++, Python, C++, C#, MATLAB/Simulink, and/or C/C++ for embedded systems).
- Simulation expertise using tools such as Gazebo, NVIDIA Isaac Sim, RViz, and MuJoCo for modeling and testing robotic systems.
- Experience with AI architectures, including LLMs, foundation models, robotic world models, SLAM, VLMs, and VLAs.
- Experience in multimodal sensor fusion, combining data from vision, depth, and other sensors for robotics applications.
- Competence in simulation environments and modeling (Gazebo, Issac Sim, 3D simulation).

Preferred Research





Background (research experience in one or more of the following areas)

- Embodied AI (Multi-modal perception, Cognition, Learning, Decision Making)
- Action Planning &
- Decision Making (Autonomous task execution, Intent prediction, Scene Reconstruction, Adaptive behavior)
- Motion &
- Movement Control (Kinematics, Dexterity, Locomotion, Balance &
- Gait Control, Manipulation, Common sense reasoning)
- Simulation &
- Testing Environments (Physics Modelling, High-Fidelity Motion Simulation, Setting Mapping)
- Human-Robot, Robot-Robot Interaction (Cognitive Interfaces, Interaction Safety, User Experience, Swarm Intelligence, Robot-Environnent interaction) Preferred Qualification
- PhD/DSc (preferred) or Master’s in Computer Science, Mechatronics, Mechanical Engineering, Robotics, Electrical/Electronics Engineering, or a related technical field.

📌 Humanoids Researcher (Bengaluru)
🏢 Infosys
📍 Bengaluru