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Semantic change, i.e. the evolution of word meanings over time, offers crucial information about historical, cultural and linguistic processes. Language acts as a mirror of societal change, reflecting evolving values, norms and technological advances. Understanding how the meaning of words evolves enables us to trace these transformations and gain a deeper understanding of our distant and recent past.This seminar explores how computational methods are revolutionizing our ability to analyze semantic change in historical texts, addressing a major challenge in the field of digital humanities. While advanced computational methods enable us to analyze vast datasets and uncover previously inaccessible patterns, few natural language processing algorithms fully take into account the dynamic nature of language, particularly semantics, which is essential for research in the humanities. As AI systems develop to better understand the historical context and dynamics of language, human annotation and interpretation remain essential to capture the nuances of language and its cultural context.In this presentation, I will show how computational and human-centered approaches can be effectively combined to examine semantic change and its links to cultural and technological developments. I will present examples illustrating how semantic change can be analyzed across temporal, cultural and textual dimensions."Methods "seminarsThe Methods Seminar is a series of seminars organized at the University of Namur with the aim of fostering interdisciplinary collaboration and knowledge exchange. All seminars take place in a hybrid format.This seminar series focuses on advanced methodological approaches, particularly in the fields of natural language processing (NLP), artificial intelligence (AI), video and image analysis, and multimodal analysis.To stay informed about details of upcoming seminars, please subscribe to our mailing list below. I subscribe to the "Methods" mailing list

More info to come."Methods "seminarsThe Methods Seminar is a series of seminars organized at the University of Namur with the aim of fostering interdisciplinary collaboration and knowledge exchange. All seminars take place in a hybrid format.This seminar series focuses on advanced methodological approaches, particularly in the fields of natural language processing (NLP), artificial intelligence (AI), video and image analysis, and multimodal analysis.To stay informed about details of upcoming seminars, please subscribe to our mailing list below. I subscribe to the "Methods" mailing list

Victimization and identity: the post-heroic society More info to come All TRANSDEM seminars

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.

For the past fifteen years or so, participatory and deliberative democracy mechanisms have been multiplying: participatory budgets, popular consultations, citizens' panels, and so on. Vincent Jacquet, a political scientist and coordinator of the European research project Citizen Impact (ERC project, European Research Council), studies the impact of these devices from the point of view of governors and citizens.

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

Trust of traditional political institutions and elected representatives, rise of authoritarian logics, definition of public services... Democracy today seems to be going through a turbulent zone. What role does the university play in this context? To shed light on this question, we interviewed four researchers from different disciplines: educationalist Sephora Boucenna, philosopher Louis Carré, political scientist Vincent Jacquet and legal scholar Aline Nardi. Their contrasting views sketch out the contours of an issue that is more topical than ever: thinking about and defending the link between university and democracy.

Florentin Rochet, a professor of computer security at the University of Namur, specializes in applied cryptography and secure communications. Against the backdrop of rapid advances in artificial intelligence and open-source solutions, he shares his analysis of the current situation and offers his outlook for the future.