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Entitled "A hidden treasure of the BUMP: the Bergeyck archive (13th-19th centuries)", this lecture by Romain Waroquier (Doctor of History - Researcher at centre PraME de l'UNamur) will lift the veil on the riches of the de Bergeyck fonds, a family archive that illustrates, in an unprecedented way, the history of the seigneury of Dhuy, which has been passed down in the same family, heir to the Counts of Namur, since the 13th century.Among these documents are several rare items hitherto unknown to historians, such as a polyptique foncier of which there is only one equivalent in the Mosan area. Exploiting these archives opens a window onto the rural and seigniorial realities of the Middle Ages in this northern corner of the province of Namur that was the seigneury of Dhuy, and whose history Romain Waroquier will retrace for us.Welcome one and all!" Practical information and registration

Training courses for training supervisors are aimed at GPs who take on students as part of the Master's program in general medicine. The aim of this space is to equip and support GPs in their role as trainers of students in the Master of General Medicine program, while encouraging the sharing of experiences between peers.TrainerFlorence PourtoisTarget audienceGeneral practitioners hosting student internsObjectivesDevelop pedagogical skills linked to clinical supervision and reflective supportFoster constructive exchanges between practitioners around the role of internship supervisorSupport the gradual integration of students into professional practiceConsolidate the partnership between the faculty and the fieldWhy participate?Because training future general practitioners is an essential and rewarding mission. Because acquiring concrete teaching tools makes the role of internship supervisor easier. And because it's an opportunity to exchange ideas with other colleagues facing the same challenges.Participation via registration on the site: http://www.mgformations.be/INAMI accreditation requiredFor further information: capmg@unamur.be

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)

The program 09:30 | Welcome speech and coffee10:00 | Presentation of the platforms11:00 | Group visit of the platforms12:00 | Lunch and networking Pysico-Chemical Characterization (PC²)The PC² platform comprises a wide range of instruments, including: liquid and solid-state nuclear magnetic resonance spectrometers, X-ray diffractometers for single crystals and powders, instruments for analyzing textural properties (nitrogen physisorption, mercury porosimetry, etc.), instruments for analyzing chemical composition (combustion chemical analysis, ICP-OES, etc.), as well as various separation techniques (chromatography, centrifugation, etc.).), instruments for analyzing chemical composition (combustion chemical analysis, ICP-OES, etc.), and various separation techniques (chromatography, centrifugation, etc.). The combination of these techniques with the presence of two research logisticians and a technician dedicated to sample analysis, as well as highly qualified researchers for the development of advanced applications, reflects the strategic intent of this platform. Among these characterization techniques, solid-state NMR and X-ray diffraction are the most advanced and unique characterization tools.Synthesis, Irradiation and Analysis of Materials (SIAM)The SIAM platform specializes in the advanced synthesis and characterization of materials and nanomaterials. It actively contributes to fundamental research in (bio)materials science, particularly in terms of characterizing surfaces, interfaces and ion/material interactions, in collaboration with international university laboratories. SIAM's analytical capabilities enable it to study a wide range of samples from fields as diverse as materials science, life sciences and heritage science. One of SIAM's key assets is its recognized expertise in spectroscopy (XPS and ToF-SIMS), which can be coupled with nuclear analysis (Ion Beam Analysis or IBA). Thanks to state-of-the-art equipment, all support is provided by a highly qualified team in a dynamic of continuous development and innovation. As part of the University of Namur, SIAM is a privileged partner both for academic research projects and for the provision of services to industrial and institutional players.Lasers, Optics and Spectroscopies (LOS)The LOS platform is developing its expertise around optical methods for the study of materials. LOS recently acquired a Raman scattering microscope for the analysis of liquids, powders, solids and thin films, both organic and inorganic. This technique can be used to identify a sample's chemical composition and structure, as well as certain properties of the medium. Raman spectroscopy can be used to characterize polymers, nanomaterials, pharmacological compounds, geological materials, precious stones, heritage objects and food products, to name but a few. In imaging mode, this technique can map the distribution of a compound in a heterogeneous sample, as well as detect traces. Practical information Registration required before November 4, 2025. I want to register Find out more about UNamur's technology platforms Contact Research Administration | Business Developer - Joël Marinozzi : joel.marinozzi@unamur.be

Nature is a spectacle! Every year, the Festival International Nature Namur (FINN) devotes an entire week to highlighting the beauty of the natural world. Its mission is clear: to amaze in order to raise awareness. Through exceptional images, the festival invites the public to discover the wealth of nature that surrounds them, and encourages them to adopt responsible behavior towards their environment.Created in 1995, FINN has become an unmissable event for lovers of nature and spectacular images. With over 35,000 visitors each year, three international competitions (photography, amateur and professional films) and a host of activities, it now ranks among the top five European events dedicated to nature.Among the highlights, the nature village brings together the festival's partners. UNamur will be hosting a stand showcasing the best shots from the Biology department's multidisciplinary trip. Fun activities will also enable the public to discover nature from a scientific angle. Brochures and information on training courses related to the festival's themes will also be available.The FINN is also organizing an Environment Day, dedicated to the major issues affecting biodiversity and human well-being. This year, it will take place on October 16 and feature three themed sessions followed by debates led by experts. For this 31st edition, the speakers will be professors Jean-Yves Storme, geologist, and Nicolas Dendoncker, geographer, as well as Manon Poignet, researcher at the Environmental and Evolutionary Biology Research Unit (University of Namur, Faculty of Science, Biology Department, ILEE Institute). More info on the FINN website

This evening is the perfect opportunity to discover our projects for the year, meet other committed young people and learn more about the opportunities offered by FUCID. See you this Monday, September 29 from 6pm at FUCID!Address: Rue Bruno 18 (la maison blanche opposite the Arsenal), 5000 Namur. More info

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

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

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

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

Take part in our open morning Given the works in the rue de Bruxelles and the renovation of part of the University parking lots, we invite you to use public transport whenever possible (train or bus) to reach Namur. UNamur boasts an ideal location, in the heart of the city just a five-minute walk from the TEC and SNCB train stations.If you're coming by car, take a look at the parking map provided. Look forward to seeing you on Saturday, June 29!