Internship R&D

We are seeking a highly motivated Research Intern to contribute to our work on task decomposition and multimodal fusion for imitation learning in robotic manipulation. This internship is ideal for students or early-stage researchers passionate about applying machine learning, computer vision, and robotics to real-world industrial automation challenges.

Responsibilities

Develop and evaluate task decomposition methods for complex robotic manipulation tasks; Investigate multimodal sensor fusion (e.g., vision, force, tactile) techniques to improve policy learning; Implement and benchmark imitation learning pipelines using simulated and real-world robotic setups; Collaborate with our engineering team to transfer research outcomes to prototype systems; Document and present findings to both technical and non-technical stakeholders.

Requirements

Enrolled in a Master’s or PhD program in Robotics, Machine Learning, Computer Vision, or a related field with a minimum GPA of 1.7 (German scale); OR GitHub repository with at least 5 stars; Familiarity with reinforcement learning, imitation learning, or behavior cloning methods; Hands-on experience with robotic platforms or simulation environments (e.g., PyBullet, Isaac Gym); Strong programming skills in Python; Excellent problem-solving skills and the ability to work independently.

Nice to have

Familiarity with PyTorch or TensorFlow

Benefits

Flexible working hours; Option to work from home when needed; A motivated team and an open corporate culture.