Master Thesis (d/f/m) On 5g/6g Non-terrestrial Networks Positioning
ATR (Airbus/Leonardo)
Ottobrunn, Bavaria, Germany
5g/6g non-terrestrial networks
Signal processing algorithms
Python and c++ programming
You will acquire an in-depth insight into PNT concepts and solutions within 5G/6G non-terrestrial networks (NTN), in order to exploit first-of-a-kind navigation demonstrators for the exploration of prototype NTN positioning solutions from concept to experimentation
Job Summary
You will acquire an in-depth insight into PNT concepts and solutions within 5G/6G non-terrestrial networks (NTN), in order to exploit first-of-a-kind navigation demonstrators for the exploration of prototype NTN positioning solutions from concept to experimentation.
Your benefits include an attractive salary and work-life balance with an 35-hour week (flexitime), an international environment with the opportunity to network globally, and the opportunity to participate in the Generation Airbus Community to expand your own network.
Your tasks and responsibilities include research on navigation concepts and algorithms for next-generation 5G/6G non-terrestrial networks (NTN), development of advanced signal processing algorithms for NTN positioning using Python and C++, and experiments using software-defined radio (SDR) on existing 5G/6G NTN demonstrators.
Matching Summary
You will acquire an in-depth insight into PNT concepts and solutions within 5G/6G non-terrestrial networks (NTN), in order to exploit first-of-a-kind navigation demonstrators for the exploration of prototype NTN positioning solutions from concept to experimentation.
Skills & Requirements
Must-have
5G/6G non-terrestrial networks
signal processing algorithms
Python and C++ programming
software-defined radio (SDR)
navigation receivers principles
Nice-to-have
international environment
work-life balance
team player
excellent communication skills
Key Requirements
Enrolled full-time student
Electrical Engineering, Telecommunication Engineering, Navigation or similar field
basic knowledge of 5G networks
experience with signal processing of field recordings