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Abstract This work demonstrates that polymer-coated gold nanoparticles can function not only as radiosensitizers but also as agents for macrophage reprogramming. Specifically, we show that these nanoparticles can repolarize tumor-associated macrophages from the immunosuppressive M2 phenotype to the pro-inflammatory M1 phenotype-a process further enhanced by clinically relevant doses of X-ray radiation. Among the four nanoparticle formulations tested, 50 nm PVP-coated gold nanoparticles were particularly effective in promoting macrophage repolarization and reducing pancreatic cancer cell viability in co-culture, both with and without radiation. These findings highlight a promising strategy to enhance the efficacy of cancer radiotherapy. Jury Prof. Julien COLAUX (UNamur), ChairmanProf. Anne-Catherine HEUSKIN (UNamur), SecretaryProf. Carine MICHIELS (UNamur)Prof. Henri-François RENARD (UNamur)Prof. Michel MOUTSCHEN (ULiège)Dr Dimitri STANICKI (UMons)Prof. Devika CHITHRANI (University of Victoria)

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

Welcome to the 1ʳᵉ MG-ERC conference This conference, linked to the research themes of the Chemistry Department, aims to bring together around 100 researchers working in the fields of heteroatom chemistry, coordination chemistry, catalysis, and inorganic chemistry. It represents a real novelty in Belgium in terms of the areas covered, and will enable participants to discover new concepts, ideas and trends in these recent areas of research in chemistry. Here is the list of speakers, who are world experts in their fieldsDr. Daniël Broere (Utrecht University, Netherlands)Prof. Agnieszka Nowak-Król (Universität Würzburg, Germany)Dr. Antoine Simonneau (Université Paul-Sabatier, Toulouse, France)Prof. Dr. Sebastian Riedel (Freie Universität, Berlin, Germany)Dr. Arnaud Voituriez (Université Paris-Saclay, France)Prof. Dr. Alessandro Bismuto (Universität Bonn, Germany)Dr. Christian Hering-Junghans (Leibniz-Institut für Katalyse, Germany)Prof. Connie Lu (Universität Bonn, Germany)Prof. Simon Aldridge (University of Oxford, UK)Dr. Ghenwa Bouhadir (Université Paul-Sabatier, Toulouse, France)Prof. Dr. Viktoria Däschlein (Universität Bonn, Germany)Prof. Viktoria Däschlein-Gessner (Ruhr-University of Bochum, Germany)Dr. Jennifer A. Garden (University of Edinburgh, UK)Prof. Muriel Hissler (Université de Rennes, France)Prof. Jean-François Paquin (Université de Laval, Canada) More information and registration

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

Every two years, the International Symposium on Marek's Disease and Avian Herpesviruses (MDAH) brings together researchers from around the world to exchange the latest insights on poultry viral diseases - covering their biology, evolution, control strategies, and epidemiology. Attendees include PhD students, postdocs and researchers representing academia, government, and commercial organizations from North and South America, Europe, Asia, the Middle East, Australia, and Africa. More information on the MDAH2026 website

Deadlines Opening of abstract submissions and registrations: September 15, 2025Deadline to submit indicative title and summary: November 30, 2025Deadline for final abstract submissions: May 1, 2026Early bird registration deadline: March 1, 2026 More information on the website

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