Master Thesis: Antenna System and Technology

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About the opportunity:

In a world where wireless networks constantly push for higher capacity and reliability, the ability to precisely shape antenna beams is a key differentiator. This thesis explores how machine learning can uncover excitation vectors that produce better beam patterns in practical antenna arrays, explicitly accounting for effects such as mutual coupling between elements. The aim is to create an excitation‑vector model that leads to improved beam‑weight design and measurable gains in overall antenna system performance. The Master's thesis project is suitable for a team of two students. 

What you will do

• Analyze the interaction between beam weights and antenna system performance, focusing on aspects such as side-lobe suppression.


• Examine the correlation between cost functions and excitation vectors across various antenna geometries.


• Investigate the influence and dependencies between excitation vectors and cost functions.


• Develop an optimized excitation-vector model.


• Assess the model’s effectiveness using established quality metrics from both machine learning and antenna design.


• Validate the model through measurement data.

This thesis integrates antenna theory, electromagnetic modeling, optimization methods, machine learning, and measurement-based validation.

The skills you bring:

• Currently enrolled in a Master's program in Engineering Physics, Electrical Engineering, Computer Engineering, or an equivalent field.


• Knowledge of electromagnetic field theory.


• Experience with Python programming.


• Experience with MATLAB.


• Experience applying machine learning methods.


• An analytical and structured approach to problem-solving.