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At Euranova, our scientific approach gives us a clear view of the issues to be addressed in the fields of AI and data science. Through our research centre, we have a firm grasp of the scientific topics we deal with and the technologies we use in our research projects and with our clients.

To share this technological knowledge with our academic and business partners and our customers, we propose tailor-made practical or applied workshops. We challenge our trainees’ everyday experiences with emerging digital opportunities. Our experts share their leading-edge knowledge and experiences with your teams.

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Releated Posts

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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Recent Posts
24.12.2025

Evaluation of GraphRAG Strategies for Efficient Information Retrieval

22.12.2025

Flight Load Factor Predictions based on Analysis of Ticket Prices and other Factors

10.09.2025

Investigating a Feature Unlearning Bias Mitigation Technique for Cancer-type Bias in AutoPet Dataset

4.08.2025

Muppet: A Modular and Constructive Decomposition for Perturbation-based Explanation Methods

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