Search results

Can we still speak of democracy when algorithms influence our electoral choices or participate in the drafting of laws? This topic is explored by Aline Nardi, researcher at the Faculty of Law and member of the Namur Digital Institute (NADI).

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

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

Researchers, graduate students, artists, designers, teachers, and creatives from all walks of life: this is a unique opportunity to take your ideas beyond the usual framework and bring them to life in a new way. The goal of this first edition: to collaboratively design an educational treasure hunt in Namur, centered on the theme of misinformation, aimed at young people aged 15 to 18.Practical detailsWhen? June 1, 2, and/or 3, 2026 (9 a.m.–4 p.m.), flexible participation (a few hours, half a day, a full day, or all three days)Where? The TRAKK in NamurLunch provided by NADI + unlimited coffee/tea in a collaborativeatmosphere Everything is set up for experimenting, testing, and creatingRegistration: free, but please send an email to digifactory.asbl@gmail.comA chance to bridge disciplines, break out of silos…and invent new ways to tell stories, convey information, and share.Note: For doctoral students, participation counts toward your doctoral program (course credits).

An innovative educational initiative was carried out at the University of Namur during the 2024–2025 academic year as part of the “Software Testing and Quality” course in the Master’s program in Computer Science, with a focus on software engineering. This initiative led to the publication of the SNAIL Report 2025, a comprehensive barometer of software development practices in the Wallonia-Brussels Federation.

Lately, and thanks to LUMI, one of Europe's largest supercomputers, the limits of our knowledge of cell lipid membranes have been pushed back. A team of Namur researchers, including Professor Benoît Champagne and Drs. Pierre Beaujean and Charlotte Bouquiaux, has just published in the Journal of Chemical Information and Modeling. The results of this study pave the way for new approaches in the field of membrane lipid therapy.

One year after receiving funding from the European Research Council (ERC) for his B-Yond project, Prof Guillaume Berionni has just been named a Fellow of the prestigious European chemistry society Chemistry Europe. This distinction makes him the new representative for Belgium for a period of 2 years.

Prof. Olivier Deparis and Dr Sébastien Mouchet (University of Namur, NISM Institute, Department of Physics) have published a book on the theme of physical optics and environmental biology. On the path of Jean-Pol Vigneron’s research, this “avant-garde” book according to Artech House, opens the door to bioinspired applications in the fields of optics, energy and the environment. Explanation.

Increasingly sophisticated technologies are invading our spheres of activity without our prior consultation as citizens. Shouldn't the new digital tools, artificial intelligence or technologies resulting from progress in neuroscience, which are transforming our identity and social relationships, be the subject of broad and sufficiently informed democratic debates? This question is at the heart of the international "research-action" project "A new humanism in the age of neuroscience and artificial intelligence" in which UNamur is participating.

This is a technical and scientific feat that has just been patented at the UNamur. Researchers from the Laboratory of Analysis by Nuclear Reactions (LARN), the Department of Physics of UNamur (NISM Institute) and the spin-off Innovative Coating Solutions (ICS) have succeeded in developing a carbon-based coating with innovative properties that can be used in a wide range of fields, including fuel cells, decoration, and mechanical parts for automobiles.

On 9 June 2023, the NISM and naXys Institutes, the Physics and Chemistry Departments and the Namur Research College were delighted to welcome Prof. Jean-Luc Brédas from the University of Arizona. A prestigious speaker of international renown, Prof. Brédas completed his doctoral thesis with Prof. Jean-Marie André at the University of Namur.