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

Dlr; Oberpfaffenhofen; Remote; EUR 6,700 - 10,000; Part time

Job summary

DLR in Oberpfaffenhofen seeks a Master Thesis candidate to advance machine learning for non-terrestrial networks. You will design ML-based receiver solutions for an IoT-LEO satellite scenario.

The project combines ML with established signal processing to push performance beyond conventional methods. Strong ML knowledge, programming skills, and an interest in satellite communications are required. Starting 01.06.2026 for six months, part-time, with TVöD-Bund remuneration.

Qualifications

• Strong knowledge of machine learning principles.
• Experience implementing and testing ML algorithms.
• Good programming skills are beneficial.
• Background on satellite communications systems.

Responsibilities

• Design ML solutions for non-terrestrial communication systems.
• Implement receiver chain for IoT LEO satellite scenario.
• Integrate ML models into established signal processing solutions.
• Collaborate with the DLR team on the project.

Skills

Machine learning

Programming

Satellite comms

Academic excellence

Tools

Python

Job description

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

• Jobs
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• DLR as an employer

Please find our joint appointments and professorships on our website: Joint appointments / Professorships

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our12,000employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge?Then this is your place!

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 offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

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.: [phone] - 28 2327

Master Thesis in Machine Learning for connectivity in Non-Terrestrial Networks Job Description Req ID: 4775 Place of work: Oberpfaffenhofen Starting date: 01.06.2026 Career level: Student research project and final thesis Type of employment: Part time Duration of contract: 6 Months

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund).