Humanoids Researcher

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, Environment 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.

Work arrangement
No