Master Thesis in Machine Learning for connectivity in Non-Terrestrial Networks

The DLR Institute of Communications and Navigation is dedicated to mission-oriented research in selected areas of communications and navigation. Its work ranges from the theoretical foundations to the demonstration of new procedures and systems in a real environment and is embedded in DLR's Space, Aeronautics, Transport, Security and Digitalization programmes.

What to expect
The Advanced Information Processing Group aims at applying state-of-the-art theoretical results into real-world applications within information processing systems. The expertise of the group ranges from quantum error correction to Smart Data Management, exploring cutting-edge communication theories such as semantic communication and Age of Information, pushing the boundaries of data utilization and dissemination.

Your tasks
In this thesis the candidate will design machine learning solutions for non-terrestrial communication systems. The main focus will be on the implementation of the receiver chain for a IoT - low Earth orbit (LEO) satellite scenario. The thesis aims to enhance the current receiver algorithms by integrating machine learning models into well-established signal processing solutions, particularly in challenging scenarios where conventional algorithms reach their performance limits.

Your profile
* Good knowledge of machine learning principles
* Previous experience in implementing and testing ML algorithms
* Good programming skills are beneficial
* Background on satellite communications systems
* Excellent acadmic records
We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 4775) please contact:

Dr. Estefania Recayte

Tel.: +49 (0)8153 - 28 2327