Research Associate in Generative AI and Robotics | Wissenschaftlicher Mitarbeitender (w/m/d) mit Promotion im Bereich Gener..., Hochschulzentrum Don...

Technische Hochschule Augsburg

Nördlingen, Bayern, Deutschland
Published Mar 27, 2026
Full-time
Permanent

Job Summary

This role at the Technology Transfer Center (TTZ) for Flexible Automation in Nördlingen offers a dynamic opportunity to bridge the gap between academic research and industrial application. As a Research Associate, you will lead and coordinate research projects focused on Generative AI and Robotics, specifically contributing to 'AI in Production' and 'Digital Production Twins.' Your daily responsibilities include conducting high-level research, presenting findings at international conferences, and teaching students within the mechanical and electrical engineering faculties. A unique aspect of this position is the strong collaboration with industrial partners and the explicit opportunity to pursue a doctoral degree (PhD) in a cutting-edge field. You will work in an agile, interdisciplinary environment that supports flexible working hours and mobile work options, making it an ideal role for an ambitious researcher looking to impact the digital transformation of production technology.

Required Skills

Education

Master's degree or Diploma in Mechanical Engineering, Production Technology, Electrical Engineering, Mechatronics, Computer Science, or a comparable field of study.

Experience

  • Professional experience in the programming and deployment of AI algorithms such as Reinforcement Learning or Vision-Language-Action models
  • Experience with data science platforms like RapidMiner or KNIME
  • Professional experience in scientific research and project coordination with consortium partners
  • Experience in academic teaching or supervising student theses is preferred
  • Proven track record in Python or C/C++ programming

Languages

German (Fluent)

Additional

  • The position is initially limited to 12 months with a maximum remuneration up to E13 TV-L. Candidates must have a high affinity for virtual learning environments and be open to interdisciplinary work.