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MASTER THESIS & PFE 2021

This document introduces you to master thesis and graduation projects supervised by our research & development department. Each project offers you the chance to be actively involved in the development of solutions to address tomorrow’s challenges in ICT and implementing them today!

If you are interested in one of our offers, please send us your application to [email protected], including your CV and motivation regarding your favourite master thesis subject (described in the document).

If you are interested in working on a topic that is not in our range of offers, we would be delighted to hear your proposition and invite you to get in touch.

Master thesis subjects and application guidelines are available here: Master Thesis Offers.

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Evaluation of GraphRAG Strategies for Efficient Information Retrieval

24.12.2025 / /
Traditional RAG systems struggle to capture relationships and cross-references between different sources unless explicitly mentioned. This challenge is common in real-world scenarios, where information is often distributed and interlinked, making graphs a more effective representation. Our work provides a technical contribution through a comparative evaluation of retrieval strategies within GraphRAG, focusing on context relevance rather than abstract metrics. We aim to offer practitioners actionable insights into the retrieval component of the GraphRAG pipeline.
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Flight Load Factor Predictions based on Analysis of Ticket Prices and other Factors

22.12.2025 / /
The ability to forecast traffic and to size the operation accordingly is a determining factor, for airports. However, to realise its full potential, it needs to be considered as part of a holistic approach, closely linked to airport planning and operations. To ensure airport resources are used efficiently, accurate information about passenger numbers and their effects on the operation is essential. Therefore, this study explores machine learning capabilities enabling predictions of aircraft load factors.
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24.12.2025

Evaluation of GraphRAG Strategies for Efficient Information Retrieval

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