Future wireless networks are expected to provide not only communication services but also sensing capabilities, enabling applications such as smart factories, autonomous systems, digital twins, security monitoring, and environmental awareness. While conventional sensing systems often rely on a single sensing node, distributed sensing networks offer the possibility to observe an object simultaneously from multiple viewpoints. By combining measurements from geographically separated sensing nodes, it becomes possible to recover richer spatial information about a target. Such multi-view sensing can potentially reveal: Object dimensions, geometric shape and surface characteristics.

These capabilities are particularly important for future 6G services which include integrated sensing and communication (ISAC), where distributed infrastructure may act collectively as a sensing network.

The objective of this internship is to investigate and develop algorithms for identifying the shape and size of objects from multiple sensing viewpoints in a distributed sensing environment.

The student will explore how measurements collected from spatially separated sensing nodes can be fused to generate a consistent representation of an observed object and estimate its geometric properties. The internship will address:

How much information about an object's shape can be extracted from multiple sensing perspectives?
What sensing features are most informative for object characterization? Such as range, Doppler, angle of arrival (AoA), reflection coefficient.
What accuracy can be achieved in estimating: Object length, width and height.

During this thesis student will have the opportunity to collaborate closely with our supportive and experienced team and to make significant contributions in your work related to signal processing, simulation for distributed-ISAC.

Required qualifications:

Following an MSc in a field related to one or more of the following: Electrical engineering, Computer Science, or Applied Computer Science.

Experience with signal processing for wireless communication, some knowledge of radar concepts is a plus.
Good programming skills MATLAB/Python
Ability to write scientific reports in English.

Type of internship: Master internship

Duration: 6 - 12 months

Required educational background: Electrotechnics/Electrical Engineering, Computer Science

Supervising scientist(s): For further information or for application, please contact Nazar Idrees (< E-Mail aus Sicherheitsgründen gelöscht >)

The reference code for this position is 2026-INT-326. Mention this reference code in your application.

Applications should include the following information: