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Master Thesis in Reinforcement Learning for Multi-Embodiment Grasping

vor 2 Monaten


Renningen, Baden-Württemberg, Deutschland Bosch Vollzeit
Job Opportunity

We are seeking a highly motivated and skilled researcher to join our team as a Master Thesis student in Reinforcement Learning for Multi-Embodiment Grasping.

Key Responsibilities
  • Design and develop architectures and pipelines to transfer current methods to online RL algorithms.
  • Create RL environments for multi-embodiment grasping using our grasp dataset generation pipeline.
  • Benchmark the current state of the art approaches in robotics grasping.
  • Work under the supervision of our research staff to guide you throughout your thesis.
Requirements
  • Master studies in Computer Science, Machine Learning, Artificial Intelligence, or comparable.
  • Proficient in Python, Machine Learning (PyTorch, JAX, TensorFlow), physics simulators (MuJoCo, Bullet, Isaac Sim), and high-level graphics libraries (Open3D).
  • Goal-oriented person with a structured and analytical mindset.
  • Fluent in written and spoken German or English.
Additional Information

This thesis requires enrollment at a university. Please attach your CV, transcript of records, examination regulations, and a valid work and residence permit if required.

We value diversity and inclusion and welcome all applications, regardless of gender, age, disability, religion, ethnic origin, or sexual identity.