For the »Self-Learning Systems« group in Nuremberg, the Fraunhofer Institute for Integrated Circuits IIS is currently seeking a


In the »Self-Learning Systems« group at Fraunhofer IIS, we combine expertise in a range of advanced machine learning topics. One of our research goals is to provide robust and safe algorithmic solutions for control and decision-making tasks for systems in complex dynamic environments. To this end, we employ deep learning in combination with reinforcement-learning techniques. These techniques are especially useful when combined with methods for reliable and interpretable artificial intelligence. We also to explore reinforcement learning with variational quantum circuits, for radio localization, beamforming and other topics from the industry.

You know how to work in radio technology and machine learning? And you think reinforcement learning is exciting?

Then you are the right person for us!

Your tasks:

  • You implement and compare reinforcement learning algorithms (e.g. value-based, policy-based, offline)
  • You work on simulations of mobile radio networks
  • You join a team working on solutions for optimization of the air interface for 5G and 6G

What we expect from you

  • You are currently enrolled in a physics, mathematics or computer science program
  • You have some machine learning (ML) background and have already worked with ML frameworks (e.g. Pytorch)
  • You have recent background in signal processing and radio
  • You are interested in deep reinforcement learning
  • You speak English fluently

What you can expect from us

  • An open and cooperative working environment
  • Collaboration in interesting and innovative projects
  • Many opportunities to gain practical experience and attend seminars
  • Flexibility concerning your working hours 

Applications are possible in German and English. Please include a cover letter, your CV and your latest transcripts of records (as PDF) and quote ID number 58252-LV. Address your application to Nina Wörlein.

Please let us know how you learned about this job opportunity.

Job Reference: 58252-LV Closing Date: