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Internships 2023

You

  • are looking for an internship in an intellectually-stimulating company?
  • are fond of feedback and continuous personal development?
  • want to participate in the development of solutions to address tomorrow’s challenges?
  • want to develop your expertise in machine learning, artificial intelligence, high-performance computing, software engineering, etc. with a dedicated international team of engineers?

 

Who We Are

As a data science consultancy market leader, we at Euranova aim at implementing active technological watch dedicated to the digital transformation of businesses.

 We EXPLORE science; through our Research & Development centre.
 We SERVE customers; through our consultancy branch.
 We CRAFT products: through the creation of international innovative products.

We offer 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. The students will work in a dedicated international team of engineers with diverse expertise in machine learning, graph theory, artificial intelligence, high-performance computing, etc.

Our Offers

Internship subjects are available below:

How To Apply

When you have gone through our internship offers, pick your favourite three. Draft a short text for each one, stating why you find it interesting and what you would do about it. Send us this statement, along with your CV, at [email protected]. Please note that the locations and dates are indicative, do not hesitate to contact us to find an arrangement. Although previous experiences using the technologies mentioned in the offers would be appreciated, it won’t be amongst the main drivers of the intern selection.

Submission deadline: we encourage you not to wait to make your application, we will process them as they come in. Note that we do not accept submissions after 31 January 2023 to guarantee the smooth running of the internship.

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.
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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.
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