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An international team of researchers has just published an article in the prestigious journal Light: Science & Applications (LSA) from the Nature group.  The teams led by Professors Michaël Lobet and Alexandre Mayer (University of Namur) collaborated with the team led by Professor Shanhui Fan, one of the leading experts in the field, from the prestigious Stanford University in California (USA).  The result: an article entitled ‘Twist-Induced Beam Steering and Blazing Effects in Photonic Crystal Devices’, or the study of beam deflection by twisting in photonic crystal devices.  Come on, let's twist light again at UNamur! 

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

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

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.

SummaryIn 2025, machine learning continues to revolutionize various scientific fields, with major implications in physics, particularly photonics. The integration of advanced machine learning algorithms has enabled significant advances in the design and control of photonic systems, improving their efficiency and performance. These advances are essential for the development of communication, imaging and quantum computing technologies. However, physics research presents many challenges that go beyond simple performance measurements: identifying patterns and building analytical models is often just as crucial.In this thesis, we apply computational intelligence tools, in particular heuristic optimization and neural networks, to develop data-driven approaches to solving various tasks in physics. Although data-centric, our approach remains rooted in physics, always seeking to understand the physical phenomena underlying the algorithms. The results of this thesis cover a wide range of applications, from the design of complex metasurfaces and diffraction gratings to the analysis and interpretation of spectral data. We have also successfully developed an optimizer capable of learning and adapting to the problems encountered, particularly in physics. This key tool in our arsenal outperforms state-of-the-art methods in our applications. In particular, it has enabled the design of a coronagraphic phase plate for exoplanet imaging, with a performance 25% better than the best previous solutions.We have also designed a compact, all-dielectric beam deflection device, operating efficiently for all polarizations, reaching a maximum efficiency of 90%. Starting from a purely data-driven design, we were able to extract and validate an analytical model based on the behavior of an echelle lattice, providing a physical understanding of its operation. In addition to simulation-based tasks, we also processed experimental data, developing an animal origin classifier for scrolls, capable of distinguishing three animal species with 90% accuracy. This tool offers a non-invasive method for conservators and historians wishing to analyze fragile historical materials.Jury members Prof. Michaël LOBET (UNamur), PresidentProf. Alexandre MAYER (UNamur), SecretaryDr. Charlotte BEAUTHIER (CENAREO)Prof. Benoît FRENAY (UNamur)Prof. Olivier DEPARIS (UNamur)Prof. Denis LANGEVIN (Université de Clermont Auvergne)Prof. Hai Son NGUYEN (Ecole Centrale de Lyon)