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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

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 event will provide students, researchers, and industry representatives with an opportunity to explore the evolution of electron microscopy, deepen their understanding of advanced analysis techniques, and participate in live demonstrations in a real research environment.Programme09:00 – 09:30 Welcome Reception with coffee and pastries09:30 – 09:45 Opening & Introduction of the Day10:30 – 11:30 Lecture with Emanuel Katzmann - Presentation: TEM / CRYO TEM.11:30 – 12:30 Lecture with Yamaguchi-san - Presentation: SEM imaging, CP and FIB sample prep.12:30 – 13:30 Lunch Break13:30 – 14:30 Lecture with Rick Verberne - Presentation: Analytical methods in electron microscopy - (EDS, windowless EDS, WDS, SXES, EBSD)14:30 – 14:45 Discover electron microscopy platform - General introduction before guided lab tour with J.-F. Colomer14:45 – 16:45 Hands-on Session - JEOL JCM-7000 Benchtop SEM - Discover how accessible and intuitive high-quality SEM imaging can be.During this session we will demonstrate:Suitable sample types and simple preparation guidelinesHow to choose the optimal observation mode (SE, BSE, low vacuum, etc.)Practical use of the instrument’s detectors and imaging featuresLive sample analysis — feel free to bring your own samples - Please inform us in advance which samples you plan to bring.Discussions, exchange ideas and NetworkingGuided Tour of the Microscopes and Laboratories16:45 – 17:15 Closing Sessions Q&A, drinks and snacks.The event offers 1 ECTS credit to PhD students and a certificate of participation upon request. I'm signing up Download the programme