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A practical training course in computational quantum chemistry was organized from May 26 to 30, 2025 as part of an ERASMUS+ collaboration between the University of Sfax and the University of Namur. This inter-university training course for PhD students in chemistry and physics from the Tunisian University brought together more than 20 students.

Abstract Radiotherapy is a cornerstone of cancer treatment and is currently administered to approximately half of all cancer patients. However, the cytotoxic effects of ionizing radiation on normal tissues represent a major limitation, as they restrict the dose that can be safely delivered to patients and, consequently, reduce the likelihood of effective tumor control. In this context, delivering radiation at ultra-high dose rates (UHDR, > 40 Gy/s) is gaining increasing attention due to its potential to spare healthy tissues surrounding the tumor and to prevent radiation-induced side effects, as compared to conventional dose rates (CONV, on the order of Gy/min).The mechanism underlying this protective effect-termed the FLASH effect-remains elusive, driving intensive research to elucidate the biological processes triggered by this type of irradiation.In vitro models offer a valuable tool to support this research, allowing for the efficient screening of various beam parameters and biological responses in a time- and cost-effective manner. In this study, multicellular tumor spheroids and normal cells were exposed to proton irradiation at UHDR to evaluate its efficacy in controlling tumor growth and its cytotoxic impact on healthy tissues, respectively.We report that UHDR and CONV irradiation induced a comparable growth delay in 3D tumor spheroids, suggesting similar efficacy in tumor control. In normal cells, both dose rates induced similar levels of senescence; however, UHDR irradiation led to lower apoptosis induction at clinically relevant doses and early time points post-irradiation.Taken together, these findings further highlight the potential of UHDR irradiation to modulate the response of normal tissues while maintaining comparable tumor control.JuryProf. Thomas BALLIGAND (UNamur), PresidentProf. Stéphane LUCAS (UNamur), SecretaryProf. Carine MICHIELS (UNamur)Dr Sébastien PENNINCKX (Hôpital Universitaire de Bruxelles)Prof. Cristian FERNANDEZ (University of Bern)Dr Rudi LABARBE (IBA)

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

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

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!