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The NaDI-CeRCLe research center has distinguished itself brilliantly on the international scene in recent weeks. Three young researchers from the EMCP Faculty have received prestigious recognition at leading international events for their research in service management: they are Floriane Goosse, Victor Sluÿters and Florence Nizette. This summer, let's discover the work of these PhD students and their significant contributions to the advancement of knowledge and practice in this field.

Measuring poverty and well-being, to better understand development inequalities between countries and better assess development policies. This is the theme on which Benoit Decerf, assistant professor in the Department of Economics and researcher at UNamur's Development Economics Research Center, is working. He has been involved in improving the poverty indicators used by the World Bank.

The morning will be devoted to presenting the detailed results of the study and putting them into perspective in the broader context of current economic, social, and environmental issues.The afternoon will give the floor to stakeholders in the field through testimonials and round tables, illustrating how the tourism sector is adapting to the challenges posed by climate change. Registration and detailed program here: Walloon tourism in the face of climate change: between risks and opportunities (January 26, 2026): Home · UNamur Event (Indico) 

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

Jury PromotersProf. Anthony Simonofski - University of NamurProf. Corentin Burnay - University of NamurOther members of the juryProf. Annick Castiaux - University of NamurProf. Lieselot Daneels - Ghent UniversityProf. Ida Lindgren - Linköping UniversityProf. Benoît Vanderose - University of NamurChair of the juryProf. Sophie Béreau - University of Namur

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

The FNRS has awarded the five-year Ernest-John Solvay Prize in the Social Sciences to Jean-Marie Baland, Professor in the Department of Economics at UNamur's EMCP Faculty and co-founder of the DeFiPP Institute's Centre de Recherche en Economie du Développement (CRED). A major accolade for a career devoted to the study of poverty, informal institutions and sustainable development..

The University of Namur takes another step forward in its commitment to supporting digital transformation. It is joining the prestigious European Research Center for Information Systems (ERCIS) network as a Partner Institution, via the MINDIT research center (Management de l'Information et Transformation Numérique).

On February 26, the Adam Smith amphitheater hosted the first major edition of Act'UNamur, a quiz on current events in 2025 organized by the Department of Social Sciences, Politics, and Communication at the EMCP Faculty. The goal? To spark students' interest in current events through a unique and fun format.

Experts highlight creativity as an essential skill for tomorrow’s decision-makers in an era of widespread AI adoption. At the same time, the cultural and creative industries represent a rapidly growing sector. To keep pace with these developments, two new tracks will be introduced into the Information and Communication and Management Engineering programs starting next fall. Focus on the expected changes in the Faculty of Economics, Management, Communication, and Political Science (EMCP).

Description Why is the history of energy so closely linked to that of social inequality? How can we frame debates on energy transition in light of conflicts over wealth distribution?For thousands of years, the use of energy has shaped human societies, structuring their hierarchies and power relations. Its control is a vehicle for emancipation as much as it is a tool of domination. Ownership of energy resources and infrastructure is a battleground for social, political, and geostrategic struggles. Depending on who owns energy, radically different societal choices can arise.But how has the link between energy and inequality developed since prehistoric times? By combining the results of research in economic history, archaeology, and climate science, Lucas Chancel seeks to show how, over the long term, the technical and political frameworks that determine energy use are linked to the distribution of wealth among individuals, social groups, and nations.The history of energy cannot be reduced to its technical dimension, nor to the sum of past political choices. It opens up a diversity of possible futures, where the decoupling of energy consumption, material resources, and prosperity is inseparable from the question of social justice.This book advocates for an ecological transition based on a collective reappropriation of energy. Drawing on experiences of wealth redistribution from the past century, it outlines an alternative to ecological disaster and extreme inequality through the development of new forms of public and participatory ownership in the 21st century. Biography Lucas Chancel is a professor at Sciences Po Paris, at the Center for Research on Social Inequality, and co-director of the Laboratory on Global Inequality at the Paris School of Economics. He has taught at Harvard University in the United States.

Nathalie Burnay, professor at the EMCP Faculty and researcher at the Transitions Institute, has just been awarded prestigious WELChange funding from the F.R.S-FNRS for her interdisciplinary research project dedicated to extending careers. This is a highly topical social issue, which she is tackling in collaboration with a team of demographers from UCLouvain.