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From 2020 to 2025, as part of her doctoral thesis in history, researcher Julie Duchêne conducted a ground-breaking investigation blending history and biology to trace the cohabitation between humans and wolves in Wallonia and Luxembourg, from the 18th to the early 20th century. Thanks to an innovative interdisciplinary approach, including DNA analysis of naturalized 19th-century specimens, her work sheds light on the mechanisms that led to the local extinction of the species. This research was made possible thanks to the support of numerous scientific and cultural partners.

As part of a research project on the medieval heritage preserved at the Moretus Plantin University Library (BUMP), the archive entrusted to it by the de Brouchoven de Bergeyck family has been meticulously studied by historian Romain Waroquier. This collection is of undeniable historical and scientific interest: hitherto unknown to researchers, it contains extremely rare documents.

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

On July 1, 2025, the F.R.S.-FNRS published the list of winners of the various doctoral and postdoctoral mandates, Télévie projects and co-financing with the Fonds de recherche du Québec. Among these, many UNamur researchers were awarded funding. UNamur's particularly high ranking rate demonstrates the quality and excellence of research on the Namur campus.

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

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.

Last June, the UNamur Board of Directors officially announced the creation of the Observatoire des Littératures Sauvages (OLSa). Founded in 2022 under the leadership of Denis Saint-Amand, FNRS qualified researcher in the Department of French and Romance Languages and Literatures, this research center studies how literature is constructed outside of books and literary institutions, through alternative objects or channels.

Entitled "A hidden treasure of the BUMP: the Bergeyck archive (13th-19th centuries)", this lecture by Romain Waroquier (Doctor of History - Researcher at centre PraME de l'UNamur) will lift the veil on the riches of the de Bergeyck fonds, a family archive that illustrates, in an unprecedented way, the history of the seigneury of Dhuy, which has been passed down in the same family, heir to the Counts of Namur, since the 13th century.Among these documents are several rare items hitherto unknown to historians, such as a polyptique foncier of which there is only one equivalent in the Mosan area. Exploiting these archives opens a window onto the rural and seigniorial realities of the Middle Ages in this northern corner of the province of Namur that was the seigneury of Dhuy, and whose history Romain Waroquier will retrace for us.Welcome one and all!" Practical information and registration

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.