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A 24-hour live stream, about ten computers and video game consoles, around fifteen participants… and one goal: to raise as much money as possible for Télévie. Building on the success of its first event, the UNamur Computer Club embarked on the adventure once again with a new charity marathon, which took place from April 7 to 8. In total, the event raised €1,831.91 for Télévie.

The past decade has seen the release of numerous mixed reality headsets. Some are aimed at casual recreational use (for example, the Meta Quest 3), while others are marketed as next-generation computing platforms (for example, the Apple Vision Pro). As these devices become integrated into our daily lives, they are redefining the way we—as human beings—interact with them.Their nature differs significantly from traditional computing devices (e.g., computers or smartphones), introducing multiple paradigm shifts driven by several factors. On the one hand, they integrate and democratize various sensors that enable the use of gaze, hand gestures, and speech as means of interaction, thereby serving as effective vectors for the adoption of multimodal user interfaces. On the other hand, their portable nature implies continuous contextual changes that fundamentally alter interface design and redefine human-computer interaction as their use becomes ubiquitous.To better understand such systems, this research is divided into three areas. First, we immerse users in a deliberately constructed pervasive environment to explore their perceptions while examining their attitudes toward managing intrusive content through manual interventions, thereby highlighting needs emerging from such contexts. Second, we explore new communication channels by leveraging metaphors and designing interaction techniques that use multiple modalities to enable more natural communication, thereby addressing the needs arising from pervasive use. Finally, after designing multimodal interaction techniques, we examine the technical requirements and review existing tools that support the development of multimodal user interfaces, identify the limitations of some of these tools, and address them by introducing a new tool called Ummi.Through these three complementary perspectives, this thesis addresses six research questions and contributes to the fields of mixed reality and multimodal interaction.The juryProf. Vincent Englebert - University of Namur, BelgiumProf. Bruno Dumas - University of Namur, BelgiumProf. Xavier Devroey - University of Namur, BelgiumProf. Marcos Serrano - University of Toulouse, FranceProf. Denis Lalanne - University of Fribourg, SwitzerlandFree event; registration required. Sign me up

Abstract Since its emergence in 1996, the Asian H5 Goose/Guangdong (Gs/Gd) lineage has circulated widely in poultry in southern China, spilling over to wild birds by 2002. Wild bird infections facilitated global dissemination via migratory waterfowl and repeated spillback into poultry, challenging the view that HPAI primarily arises from LPAI mutation. Subclade 2.3.4.4b emerged in Asia in 2013, reached Europe in 2016, caused recurrent epizootics, diversified into multiple genotypes, became dominant in wild birds, and shows zoonotic potential.This thesis investigates critical knowledge gaps regarding H5Nx subclade 2.3.4.4b in poultry: (1) early within-flock spread after punctual introduction in chickens, particularly during the first European epizootics; (2) influence of pre-existing immunity on silent circulation; (3) limitations in diagnostic throughput during epizootic peaks; (4) potential of environmental surveillance, including air and dust sampling; and (5) impact on egg contamination and the reproductive tract, relevant for food safety and zoonotic risk.Four main objectives were addressed: (1) development of an experimental model simulating punctual introductions and spread, comparing 2017 and 2020 strains and assessing pre-existing immunity; (2) enhancement of diagnostic capacity via alternative sampling, semi-automated RNA extraction, and high-throughput processing; (3) evaluation of air and dust sampling for virus monitoring under experimental and field conditions; and (4) assessment of egg contamination risk. Alternative sampling and environmental monitoring were also applied to Newcastle disease virus as a comparative notifiable pathogen. Jury Prof. Tuci Elio - University of Namur, BelgiumProf. Anthony Cleve - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Alvaro Gutierrez - Universidad Politecnica de Madrid, EspagneMr. Fabian Duchesne - Qualitics SPRLProf. Anders Lyhne Christensen - SDU, Denmark Evènement public et gratuit - Inscription obligatoire Je m'inscris

To mark International Women's Day, celebrated on March 8, we invite you to discover the portraits of seven inspiring women from the university's seven faculties. Throughout March, a series of portraits of women from the university will be on display in various areas of the campus. Conceived and produced by four UNamur students and coordinated by the University Community Life Service (VéCU), this project offers an inspiring showcase for the careers, voices, and commitments of these women who bring the institution to life on a daily basis.  

Abstract Deep learning has revolutionized computer vision in recent years and has been applied to many fields. This thesis focuses on medical endoscopy, where deep learning can assist physicians in many tasks, such as navigating the lungs during bronchoscopy, assisting in the detection of lung diseases, detecting Crohn's disease from capsule endoscopy (PillCam), or automating the detection of polyps during colonoscopy procedures.This thesis, entitled From Pixels to Practice: Deep Learning for Endoscopy, explores how modern neural networks and learning paradigms can improve visual understanding in endoscopy, with the aim of contributing to computer-aided detection (CAD) systems that can be integrated into clinical workflows.This work follows an article-based structure and links methodological advances in geometric and temporal modeling to techniques for handling data scarcity and imbalance, as well as to the practical and clinical implications of deep learning for lung tumor detection, both from a clinical and practitioner perspective. The first part of the manuscript provides a common foundation for all subsequent parts. First, we present a general introduction to the field of machine learning in Chapter 1, explaining concepts such as classification, loss functions, and artificial neural networks. Next, Chapter 2 focuses on the field of deep learning for computer vision, detailing the main vision tasks, the concept of convolutional neural networks, ResNet, and U-Net. Finally, Chapter 3 describes medical imaging, with a focus on computed tomography (CT) scans and optical imaging. The second part of the thesis focuses on learning spatio-temporal representations. In Chapter 4, we use deep neural networks combining spatial features and temporal recurrence to address the problem of detecting the bronchial carina, an anatomical landmark that helps doctors navigate the lungs. By evaluating classification (ResNet-50), segmentation (nnU-Net), and recurrent (GRU) models on a bronchoscopy dataset we created, the study highlights the benefits of combining information from segmentation masks and temporal features. Chapter 5 continues the segmentation task by analyzing the extent to which rotation-equivariant U-Nets, based on E(2)-CNNs with C4, C8, and D4 symmetry groups, can improve performance when the orientation of objects in the image is arbitrary. Together, these chapters show how temporal and geometric modeling capture complementary aspects of visual structure. They further highlight that data imbalance and scarcity are recurring problems in deep learning. The third part studies learning in situations of data scarcity and imbalance. First, Chapter 6 explores supervised contrastive pre-training [1] on large, domain-close endoscopic datasets (Hyper-Kvasir [2], LDPolyp [3]), which is then transferred to smaller, disease-specific data (Crohn-IPI [4]). This methodology performs better than pre-training on ImageNet or based on cross-entropy, highlighting the value of domain-specific contrastive representations. Next, Chapter 7 introduces Mask-Aware Cropping (MAC), a new data augmentation technique that mitigates pixel-level imbalance in segmentation. On various datasets with varying imbalance regimes (URDE [5], Kvasir-SEG [6], HAM10000 [7]), MAC consistently improves Dice and IoU metrics under conditions of extreme imbalance. Together, these methods form a data-centric framework for effective learning when annotations are scarce or unevenly distributed. The fourth part of the thesis focuses on deep learning in the operating room. Chapter 8 proposes a first model (ResNet-50) for the visual detection of lung cancer in bronchoscopy, trained on real, in-vivo data. The model outperforms junior physicians, while remaining inferior to experts. This result shows that CAD systems for lung cancer detection are promising. Chapter 9 extends this work by evaluating the usability of a CAD system based on a deep learning model. Combining probability indices, temporal graphs, and saliency map overlays, a multicenter evaluation with 10 physicians is conducted. The tool received favorable feedback, with high usability (SUS score of 80.5 [8]) and strong clinical acceptance. Beyond endoscopy, the results concerning rotation equivariance and pixel imbalance can be generalized to other fields such as microscopy, dermatology, and aerial imaging. This shows that the proposed methods are applicable to visual learning under structured variability and limited data constraints.Keywords: machine learning, computer vision, medicine, endoscopy, convolutional neural networks, segmentation, recurrent models, equivariance.  Jury Prof. Bruno Dumas - University of NamurProf. Frénay Benoit - University of NamurProf. Schobbens P-Y. - University of NamurProf. Beuls Katrien - University of Namur,Dr. Benjamin Mertens - Lys MédicalProf. Oramas Mogrojevo José Antonio - University of AntwerpDr. Mancas Matei - University of Mons Register

May 5, 2026 | Registration for Thibaut Septon’s public thesis defense Name First name E-mail address Will attend the reception following the defense Yes ( optional ) No ( optional ) Need a parking sticker Yes ( optional ) No ( optional ) Would like a certificate for defense assistance Yes ( optional ) No ( optional ) In order to process your request, you must complete all fields marked "optional". When you submit this form, the completed data will be transmitted to UNamur and used to process your request. Learn more about your data protection and your rights This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

At a time when misinformation, post-truths and conspiracies are undermining confidence in science, UNamur welcomes SPiN (Science & Philosophy in Namur), a new interdisciplinary research center that questions the place of science in society. Founded last September by Olivier Sartenaer, Professor of Philosophy of Science at UNamur, SPiN brings together philosophers and scientists around a common vision: to develop a critical and accessible reflection on science in all its diversity..

JuryProf. Guillaume BERIONNI (UNamur), PresidentProf. Stéphane VINCENT (UNamur), SecretaryProf. Carmen GALAN (University of Bristol)Dr. Louis FENSTERBANK (Collège de France)Prof. Raphaël ROBIETTE (Université catholique de Louvain)AbstractCarboxylic acids are ubiquitous in nature and inexpensive compounds. Decarboxylation has become a key chemical transformation and has been widely reported in organic chemistry except for carbohydrates. This reaction can be catalyzed by transition metal and can also be induced by light, thermal activation, or photocatalysis. Borylated compounds have stimulated the pharmaceutical industry's interest (Boromycin, Bortezomib or boron neutron capture therapy). Recent methodologies have been developed to transform carboxylic acids to boronate esters by metal-catalyzed or light-promoted or photocatalyzed reactions. In this thesis, we explored the synthesis of borylated carbohydrates through a decarboxylation pathway. More specifically, sialic acids being among the most important carbohydrates in glycobiology, we addressed the problem of the synthesis of borylated sialic acids. On the other hand, organophosphates play an important role in diverse fields: in materials chemistry, in agriculture, in organic chemistry, and in biochemistry. Phosphorylation is a key reaction in biological processes such as signal transduction and cell activity regulation. The formation of phosphorylated carbohydrates has been widely described through two-electron mechanisms. However, radical phosphorylation of carbohydrates remains unexplored. This Ph.D. thesis describes the development of new methodologies for the decarboxylative functionalization of carbohydrates, focusing on borylation and phosphorylation..

Our keynote speakers for 2026 are Professor Roosmarijn Vandenbroucke (Ghent University) and Professor Nelly Litvak (Eindhoven University of Technology). More information on the "Women in Science" website

The F.R.S.-FNRS has just published the results of its various 2025 calls for proposals. These include the "Credits & Projects" and "WelCHANGE" calls, as well as the "FRIA" (Fund for Research Training in Industry and Agriculture) and "FRESH" (Fund for Research in the Humanities) calls, which aim to support doctoral theses. What are the results for UNamur? Twenty-eight projects have been selected, demonstrating the quality and richness of research at UNamur. 

Terminator to talk about AI? Wall-E to talk about technological dependence? The Truman Show to discuss social media? In a new book, two professors from UNamur, Anthony Simonofski (digital transformation—EMCP Faculty—NaDI Institute) and Benoît Vanderose (software engineering—Faculty of Computer Science—NaDI Institute), take readers on a journey at the crossroads of digital technology and cinematic imagination. 

A Hackathon is a short event where teams reflect on a particular theme. They attempt to find solutions by developing innovative projects. At the end of the event, a jury determines which projects have caught their attention and thus won the competition. A CSLabs initiative The Computer Science Labs is a junior enterprise that grew out of the Faculty of Computer Science at the University of Namur. In practical terms, its actions revolve around carrying out projects, training members on IT-related topics and organizing events. Read more