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This thesis presents a novel approach to address the challenges of deploying and managing Network Functions Virtualization (NFV) in resource-constrained and multi-domain environments. The proposed solution leverages a Raspberry Pi clusterbased approach for NFV deployment in resource-constrained environments, combined with a deployable Sidecar VNF (S-VNF) coordinator for multi-domain NFV orchestration.The thesis demonstrates the feasibility of integrating NFV into edge computing environments by successfully deploying and managing Network Services (NSs) on a Raspberry Pi cluster. The S-VNF coordinator facilitates efficient cross-cloud NFV deployment and management while ensuring security and interoperability.While the obtained deployment and scaling delays in the testbed setup were significant due to the bare-metal deployment process used, the proposed solution remains valuable in environments where service maintenance time is a critical factor.By automating deployment and scaling, organizations can minimize the impact of service maintenance time, improve customer satisfaction, and enhance system resilience. Moreover, the solution enables NFV to be deployed effectively in edge environments, providing benefits such as reduced latency and improved network performance.Overall, this thesis contributes to the advancement of NFV by providing innovative solutions for deployment and management in challenging environments. The proposed framework has the potential to enable the widespread adoption of NFV and drive the development of new network services.Directed by Prof. Laurent SCHUMACHER and Prof. Sokchenda SRENG.In front of a jury composed of:Prof. Wim VANHOOF, President, University of NamurProf. Laurent SCHUMACHER, Co-Promoter, University of NamurProf. Sokchenda SRENG, Co-Promoter, ITC Graduate School (Cambodia)Prof. Florentin ROCHET, Internal Member, University of NamurProf. Johann MARQUEZ-BARJA, External Member, University of AntwerpProf. Bruno QUOITIN, External Member, University of MonsProf. Raveth HIN, External Member, ITC Graduate School (Cambodia)You are cordially invited to a drink, which will follow the public defense.For good organization, please give your answer by Thursday March 20 by means of this link.Contact: Daelman Isabelle - isabelle.daelman@unamur.be

Instantly obtain a translation in sign language (LSFB) of a word written in French: that's what MOSI (Du mot au signe) makes possible. This new tool is the fruit of a collaboration between the University of Namur, the asbl École et Surdité and the asbl LSFB, supported by the King Baudouin Foundation.

Critical thinking, the art of productive doubt, can be learned and cultivated. Faced with information overload and the spread of artificial intelligence, it is more important than ever for students to develop this skill throughout their studies. At UNamur, this educational necessity takes many forms. 

Japan is almost 10,000 kilometers from Belgium, a country that fascinates, not least for its rich culture full of contrasts. Researchers at UNamur maintain close ties with several Japanese institutions, particularly in the fields of computer science, mathematics and video games. Let's take a look at some of these collaborations..

This is great recognition for the excellence of the research carried out at the University of Namur: the Faculty of Informatics has been asked to join the prestigious Informatics Europe network, which brings together the most dynamic departments and faculties of Informatics across Europe.

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

The Namur Digital Institute (NaDI) is launching a series of original events: "Les Séances du Numérique". Films followed by debates with experts to understand digital challenges and stimulate collective thinking. A project spearheaded by Anthony Simonofski, Anne-Sophie Collard, Benoît Vanderose and Fanny Barnabé.

Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

Something for everyone 09:30 | Welcome ceremony for new students11:00 | Back-to-school celebration at Saint-Aubain Cathedral (Place Saint-Aubain - 5000 Namur), followed by student welcome by the Cercles. Read more

BNAIC/BeNeLearn 2025 will be held at the University of Namur under the auspices of the Belgian-Dutch Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS). The conference aims at presenting an overview of state-of-the-art research in artificial intelligence and machine learning in Belgium, The Netherlands, and Luxembourg. More information and registration