Thesis: Robot grasp recommendation system using Deep Q-Networks (m/f/d)

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Key Responsibilities:
  • Develop deep reinforcement learning models like DQN and RLHF
  • Collect and annotate real-time data from the vision models deployed in the robot cell
  • Modify the deep learning models for vision-based state-action space
  • Furthermore, modify DQN to act as a recommendation system
  • Computer Science, Information technology, Robotics, and related studies
  • Experience in Python, PyTorch, and Git for programming
  • Familiarity with computer vision, reinforcement learning, and deep learning
  • Fluent English communication skills
  • Similar programming records on GitHub or any other open platforms
  • Experience working with robots and 3D cameras
  • Knowledge of mathematics for intelligent systems

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Your benefits

Sereact is a pioneer in AI-powered robotics for autonomous warehouses. With our innovative pick-and-pack automation solution, we assist logistics businesses like Daimler Truck, Zenfulfillment or Material Bank in boosting productivity and minimizing costs of their intralogistics. Join our dedicated team and contribute to the growth of this exciting industry.

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