Internship Offers 2026-2027
Every year, Euranova opens master's thesis and end-of-study internship tracks embedded directly within our consulting practices and prototyping lab. Explore our internship opportunities.
Beyond the illusion of anonymity: a pragmatic approach to data privacy
Stripping obvious identifiers from your datasets is rarely enough to protect user privacy or avoid massive regulatory fines. Discover a structured, risk-based blueprint for true data anonymization that perfectly balances rigorous legal compliance with real business utility.
GraphRAG: unlocking enterprise knowledge with knowledge graphs
Standard RAG struggles with complex enterprise queries. Discover how GraphRAG uses Knowledge Graphs, ontology-driven pipelines, and AI agents for smarter retrieval.
Who judges the AI? Fine-tuning Mistral 7B as a specialized evaluator
As organizations scale their Generative AI from prototypes to production, verifying output quality has become the next major cost bottleneck. Do you really need a 'frontier' LLM to judge your AI? We investigated whether a smaller, specialized model could do the job.
Mastering Sovereign AI & Local LLMs
Don’t Let Your Data Become a Liability: navigating the evolving landscape of data modeling
This article serves as a practical guide to choosing the right architectural blueprint to bridge the gap between chaotic raw data and structured insights.
Sovereign compute at scale: architecting for the Belgian AI Factory Antenna
The Belgian AI Factory Antenna gives SMEs and startups unprecedented access to EuroHPC supercomputers, but it comes with strict access rules and technical trade-offs. Discover how to architect your AI workflows for the EuroHPC landscape in our latest guide.
Pioneering the future of aerial intelligence through advanced 3D digital twins
How do we inspect critical infrastructure safely and efficiently? To tackle this, dive into the combination of aerial platforms with advanced AI to create photorealistic, queryable 3D Digital Twins.
Quantifying Retrieval Quality in GraphRAG: A Schema-Agnostic Approach
In this paper, we propose a novel schema-agnostic framework for the automated generation of synthetic evaluation datasets from KGs. Unlike previous approaches, our framework establishes a rigorous, deterministic ground truth to specifically quantify the retriever performance across nine distinct query categories, including multi-hop and aggregation tasks.
Navigating the AI transition in marketing
IEEE Big Data 2025: the shift from scale to smart
IEEE Big Data 2025 signals a shift to secure, hybrid intelligence. CTO Sabri Skhiri unpacks the engineering reality from the conference: the practical shift to embeddings, the real need for security layers, and the limitations of AI agents in production.
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.
Oncologie : des médicaments microbiomiques basés sur les données
Le séquençage du microbiome et les modèles prédictifs favorisent l'innovation en immunothérapie.
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.
Breaking data barriers
A major automaker pursued a rapid digital overhaul, requiring strong data governance to break silos, ensure GDPR compliance, and enable a scalable data‑mesh foundation.
Beyond the Cloud, Advanced AI Computer Vision
From Cloud to Edge: how to transform your business model with Edge AI? Dive into embedded computer vision and edge AI algorithm development with STMicroelectronics STM32 and Euranova.
Investigating a Feature Unlearning Bias Mitigation Technique for Cancer-type Bias in AutoPet Dataset
We proposed a feature unlearning technique to reduce cancer-type bias, which improved segmentation accuracy while promoting fairness across sub-groups, even with limited data.
Beyond the hype: how NVIDIA GTC Paris 2025 trends are shaping industry
NVIDIA GTC Paris 2025 revealed an unprecedented scale and breadth of innovation, with a clear focus: not on predicting the future of AI, but on demonstrating how existing technologies are being put to work today. Our CTO Sabri Skhiri was on the ground to bring back insights.
Muppet: A Modular and Constructive Decomposition for Perturbation-based Explanation Methods
The topic of explainable AI has recently received attention driven by a growing awareness of the need for transparent and accountable AI. In this paper, we propose a novel methodology to decompose any state-of-the-art perturbation-based explainability approach into four blocks. In addition, we provide Muppet: an open-source Python library for explainable AI.
Smarter dispatch of technicians
A telecom provider set out to improve service efficiency by predicting the ideal technician for each job, reducing costly misassignments while boosting customer satisfaction through smarter, data‑driven dispatching.
Tech insights from GTC Paris 2025
Among the NVIDIA GTC Paris crowd was our CTO Sabri Skhiri, and from quantum computing breakthroughs to the full-stack AI advancements powering industrial digital twins and robotics, there is a lot to share!
How consolidated data contributed to create a 360 customer experience
Dive into a solution capable of hosting millions of customer data while providing a comprehensive, up-to-date view of customers and their insurance policies.
The LLM coding revolution needs a rulebook
Teams ship AI-generated code faster than ever — and accumulate debt just as fast. The problem isn't the tools. It's the missing process around them.