Skip to content

Internship & Master Thesis Offer – 2021

Our master thesis and internships offers for the coming year, supervised by our software engineering department or by our research & development department, will be available in the course of November, and will cover the following research topics:

 

Regarding data privacy: 

  • Legal entity relations with knowledge graph
  • Legal NLP
  • Privacy by design
  • Topic modeling
  • Text summarisation

 

Regarding data automation

  • GAN for multimodal representation
  • AutoML
  • Optimization methods
  • Computer vision
  • Graph Embeddings

 

Regarding data pipelines

  • Reinforcement learning
  • Optimisation methods
  • Stream Processing
  • CEP
  • Network compression

 

Regarding data quality

  • Denoising technique
  • GAN for missing data
  • Semi-Supervised learning
  • Data cleaning
  • Attention Model for Structural dep.

 

Each project is an opportunity to feel both empowered and responsible for your professional development and to address tomorrow’s challenges in ICT, coached by the Eura Nova crew. The detailed offers will be available mid-november. In the meantime, do not hesitate to contact us at career@euranova.eu for any question regarding internships and master thesis!

As an example, the documents listed below present our 2020 master thesis and internships:

Share on linkedin
Share on twitter
Share on email

Releated Posts

INTERNSHIPS 2022

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.
Read More

Multimodal Classifier For Space Target Recognition

We propose a multi-modal framework to tackle the SPARK Challenge by classifying satellites using RGB and depth images. Our framework is mainly based on Auto-Encoders to embed the two modalities in a common latent space in order to exploit redundant and complementary information between the two types of data.
Read More