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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)

The first global conference of Chinese materials researchers took place from July 22 to 28, 2025, at the University of Namur. Organized by Professor Bao-Lian Su, director of the Inorganic Materials Chemistry Laboratory (CMI) of the Nanomaterials Chemistry Unit (UCNano) in the Chemistry Department at the University of Namur, Belgium, in collaboration with Professor Qing-Jie Zhang of Wuhan University of Technology (China) and Professor Max Gao-Qing Lu of the University of Wollongong (Australia), the event brought together nearly 500 participants. 

A project submitted by Professor Frédéric Silvestre's Laboratoire de Physiologie Évolutive et Adaptative (LEAP) at the University of Namur has been ranked among the top 6 research projects funded by the FNRS and the Fonds de recherche du Québec (FRQ) for scientific collaboration between Wallonia and Quebec. The aim? To understand the impact of human activities on St. Lawrence Estuary (SLE) belugas, using interdisciplinary approaches to help improve conservation strategies for this threatened species..

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. 

Two researchers from the University of Namur's Department of Physics, Professor Michaël Lobet and his PhD student Adrien Debacq, are taking a close look at a subject that fascinates the scientific community : superradiance in media with a refractive index close to zero. In an article published this summer in Nature's prestigious journal Light: science & applications, in collaboration with Harvard University (USA), Michigan Technological University (MTU) and Sparrow Quantum, they contribute to the development of quantum computing.

Essay topic Essays on the Empirical Analysis of Crypto-Assets: Market Efficiency, Peg Failures, and Financial Flights Composition of the Jury Promoter: Prof. Jean-Yves Gnabo (UNamur)Other jury members: Prof. Sophie Béreau (UNamur)Prof. Kris Boudt (UGent)Prof. Sarah Bouraga (EM Normandie)Prof. Jérôme Lahaye (Fordham University)Jury president: Prof. Corentin Burnay (UNamur)

Rubber boots, overalls, the bleating of ewes, the smell of straw... The scene is set. On the farm of the Centre de Recherche Ovine, located in the Namur countryside, veterinary students live, for the space of a few days, to the rhythm of lamb births. An intense, formative experience, filled with technical gestures and emotions, supervised by veterinarians from the University of Namur.

Abstract Primarily described as an alarmone, secondary messenger (p)ppGpp, when accumulated, binds to many targets involved in DNA replication, translation, and transcription. In the asymmetrically-dividing a-proteobacterium Caulobacter crescentus, (p)ppGpp has been shown to strongly impact cell cycle progression and differentiation, promoting the non-replicating G1/swarmer phase. Mutations in the major subunits of transcriptional complex, b or b' subunits, were able to display the (p)ppGpp-related phenotypes even in the absence of the alarmone. We identified that the transcriptional holo-enzyme, RNA polymerase (RNAP) is a primary target of (p)ppGpp in C. crescentus. Furthermore, mutations that inactivate (p)ppGpp binding to RNAP annihilated the (p)ppGpp-related phenotypes and phenocopied a (p)ppGpp0 strain. Our RNAseq analysis further elucidated the changes in the transcriptional landscape of C. crescentus cells displaying different (p)ppGpp levels and expressing RNAP mutants. Since the DNA replication initiation protein DnaA is required to exit the G1 phase, we observed that it was significantly less abundant in cells accumulating (p)ppGpp. We further explored its proteolysis under the influence of (p)ppGpp. Our work suggests that (p)ppGpp regulates cell cycle and differentiation in C. crescentus by reprogramming transcription and triggering proteolytic degradation of key cell cycle regulators by yet unknown mechanisms. In Part II, we identified two σ factors belonging to the ECF family that might be involved in this (p)ppGpp-accompanied phenotypes. In Part III, we propose an overlapping role of the ω subunit, RpoZ, and the heat shock subunit, RpoH, in carbon metabolism.JuryProf. Gipsi LIMA MENDEZ (UNamur), PresidentProf Régis HALLEZ (UNamur), SecretaryDr Emanuele BIONDI (CNRS-Université Paris-Saclay)Prof. Justine COLLIER (University of Lausanne)Dr Marie DELABY (Université de Montréal)