Jobs / Summary

Working Student - Robot Control and Learning for Humanoid Robots

Confidential company · Munich · Posted Jul 7, 2026

Public summary

Join a high-tech startup in Munich focused on integrating AI and robotics by developing humanoid robots with advanced sensing and vision capabilities. This role suits master's students passionate about robot control and machine learning, offering the chance to engage in cutting-edge research and development in humanoid robotics. Work includes developing control algorithms, simulation, experimentation with real robots, and interdisciplinary collaboration in an agile and diverse environment.

Location and work setup

Location
Munich
Remote status
On-site
German requirement signal
No German Required Detected
Detected job language
English

Responsibilities

Develop and improve control and learning algorithms for humanoid robots; work with simulation environments for training and benchmarking; support reinforcement, imitation, and model-based learning pipelines; implement locomotion, manipulation, balancing, and whole-body control behaviors; analyze robot behavior and learning performance; assist sim-to-real transfer and robotic hardware experiments; integrate learned policies with perception, planning, control, and actuation; review and reproduce robotics and machine learning research; clearly document and present technical findings.

Qualifications

Currently enrolled in a Master's program in robotics, computer science, electrical or mechanical engineering, AI, or related field; strong interest in humanoid robotics and robot learning; proficient in Python and/or C++; basic knowledge of robot kinematics, dynamics, control, motion planning; familiarity with machine learning techniques like reinforcement and imitation learning; experience with simulation tools or robotic frameworks is advantageous; ability to work independently, quickly learn, and collaborate effectively in a team setting.

Skills

robot control robot learning simulation Python programming C++ programming robotics frameworks reinforcement learning imitation learning machine learning robot kinematics robot dynamics motion planning technical communication research review