Skip to content

Internships 2020

This document presents internships supervised by our software engineering department or by our research & development department. Each project is an opportunity to feel both empowered and responsible for your own professional development and for your contribution to the company.

 

If you are interested in one of our offers, please send us your application to [email protected], including your CV and motivation regarding your top three internship positions (described in the document).

 

If you wish to read the testimonies of students who have done an internship at EURA NOVA, visit our blog, or read directly their experiences:

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 get in touch.

Internship subjects and application guidelines are available here: Internship Offers.

Releated Posts

Evaluation of GraphRAG Strategies for Efficient Information Retrieval

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.
Read More

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

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.
Read More