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The discovery of nuclear energy marked a turning point in human history. Today, alongside debates about its role in energy production and its destructive potential, nuclear energy continues to be used in a wide range of fields, such as medical research and cancer therapies. At UNamur, nuclear energy is thus at the heart of the work of biologists, physicists, and art historians.

Geology students at UNamur are encouraged to continue their studies at another university after completing their bachelor’s degree. This transition has been successful, as recently confirmed by two professors from KULeuven who visited the students.

As part of the interdisciplinary course “AI: Challenges and Opportunities,” students at UNamur presented a series of posters focusing on the uses of artificial intelligence in their fields of study. This highlight showcased the diversity of AI applications, as well as the University of Namur’s commitment to training students to use it critically, fairly, and thoughtfully.

In April 2026, UNamur hosted two events as part of the UNIVERSEH alliance, bringing together more than a hundred students from across Europe.

The University of Namur and STÛV, a Namur-based company specializing in wood and pellet heating solutions, are celebrating ten years of fruitful collaboration. This partnership illustrates the importance of synergies between academia and industry to improve competitiveness and meet environmental challenges.

Information technology plays a significant role in our daily lives. Life without a computer or cell phone seems unimaginable to us. But information technology serves many other fields, such as medicine, management, the environment, agriculture, space, biology… and its role in new sectors is constantly growing. Get ready to shape the future of our society in a young, dynamic, and rapidly expanding field  

JuryProf. Joseph WINKIN (UNamur), ChairProf. Timoteo CARLETTI (UNamur), SecretaryProf. Alexandre MAUROY (UNamur)Prof. Malbor ASLLANI (Florida State University)Dr. Maxime LUCAS (UNamur)Dr. Riccardo MUOLO (RIKEN Institute)AbstractSynchronization is a ubiquitous phenomenon in the world around us. It is a crucial feature that ensures the proper functioning of many complex systems. The various generators in a power grid must produce alternating current at a common frequency, and the brain’s cortical regions synchronize their activities to enable the brain to control the human body. These systems can be modeled as coupled oscillators, as in the famous Kuramoto model, where entities interact in pairs so that they synchronize globally.However, synchronization can also pose a problem. For instance, excessive synchronization of brain dynamics leads to pathological states such as epileptic seizures. It is therefore necessary to develop methods that reduce global synchronization by locally controlling the dynamics of certain oscillators. In particular, a control scheme based on a Hamiltonian framework has been designed to effectively desynchronize the Kuramoto model.Nevertheless, some limitations remain. First, the controlled nodes are selected at random without considering their specific characteristics. Second, this method is designed to control systems with a network structure—that is, with pairwise coupling—whereas many recent studies have demonstrated the importance of higher-order networks, i.e., group interactions, in modeling such systems.In this Ph.D. thesis, we aim to address these gaps through several studies. We explore the optimal method for selecting controlled nodes to maximize control efficiency, investigate the method’s ability to desynchronize systems with higher-order interactions, and develop a new control method tailored to this framework.Our results not only improve these control techniques but also offer novel perspectives on the synchronization of complex systems. They allow us to better understand the influence of each local entity on collective behavior and the role played by interactions of different orders. Among other things, they shed light on the non-monotonic relationship between synchronization capacity and the strength of higher-order interactions.

September 14, 2026 | Program for Everyone 9:00 a.m. | Welcome at Pedro Arrupe (Rue de Bruxelles, 67 - 5000 Namur).11:00 a.m. | Back-to-school celebration at Saint-Loup Church—to be confirmed, rue du Collège, 5000 Namur—followed by a welcome for students by the student clubs. September 15, 2026 | Faculty of Science Welcome Sessions Hosted by the Faculty in S01 (Dean and Associate Dean)Sessions 1: Biology and Veterinary Science from 8:30 a.m. to 9:30 a.m.Block 1: Math, Physics, Chemistry, and Geo² (Geology and Geography) from 10:00 a.m. to 11:00 a.m.Welcome sessions hosted by the departmentsMathematics: at 11:00 a.m. in Room S09, followed by a complimentary lunch in the ground-floor lobby of the Faculty of SciencesPhysicists: starting at 2:00 PM in S05Chemists: starting at 11:15 a.m. in CH12, followed by lunch (location to be confirmed / usually in the CH12 lobby)Geo²: starting at 11:00 a.m. in the Sciences Academic Room—5th floor of the Sciences buildingBiology: 9:35 a.m. – Tour of the lab rooms, departing from the Medicine Courtyard; lunch break – Sandwiches provided by the Biology Department in the Medicine Courtyard; 2:00–3:30 p.m. – Welcome by the Chair of the Biology Department in CH01VT: Starting at 9:30 a.m., welcome session hosted by the department: Information is available on the WebCampus pages SC2026 and SVET B002, as well as on the Department of Veterinary Medicine’s Facebook page.Please note that these welcome sessions are for first-year student newcomers.Separate orientation sessions for returning students are also scheduled at other times.  Some courses will begin as early as Tuesday, September 15. Schedules are currently being finalized and will be available as soon as possible on WebCampus - SCIEINFO, the primary platform for disseminating administrative information within the Faculty of Sciences.  And before school starts? Give yourself the best possible chance—enroll in preparatory courses!Preparatory courses allow you to get a taste of university-level education by reviewing the subjects essential to your future program of study.Designed exclusively for students graduating in 2026, these preparatory courses—scheduled between mid-August and early September—are tailored to each university program.Learn more about the schedules for the different sessions and register for the preparatory courses...  To make the most of your first semester at the university, be sure to attend the orientation days!The orientation days will take place on September 10 and 11, 2026.On the agenda: a tour of the campus and the city, a student housing fair (KàPs), themed workshops (student rights, digital tools, budgeting, student engagement, sports, and more), a barbecue, and a super-enthusiastic team ready to help you discover life on campus!Thursday, September 10: 7:30 a.m. – Student check-in; 8:30 a.m.–12:40 p.m. - Workshops for students; 12:40 p.m. – Lunch – Barbecue available by registration (€3.60 per sausage sandwich / falafel sandwich) and a bar run by the AGE; 2:00–6:00 p.m. - Project-based student housing fair; 8:30 p.m.–1:00 a.m. – Bunker reserved for first-year studentsFriday, the 11th: 8:30 a.m.–12:30 p.m. - Workshops for students; 12:30 p.m. - Lunch; 2:00 p.m. - Depending on the faculty: organized by the faculty or open to all.Reserved for new students - Information will be sent after registration at UNamur.   

On June 23, 2026, the F.R.S.-FNRS published the list of recipients of various doctoral and postdoctoral fellowships and Télévie projects (cancer-focused research). Among them, numerous researchers from UNamur received funding.

Recognized as a leading research conference in the field of organic electroluminescence and light-emitting devices, the ICEL conferences have generally been held every two years since their inception in Fukuoka, Japan, in 1997, by Professor Tetsuo Tsutsui. A look back at ICEL2026, the 15th conference of its kind, held at UNamur. 

Join a community of innovators, explore cutting-edge technologies, and get ready to shape the digital world of tomorrow! Ready to code your success? Welcome to the Faculty of Computer Science at UNamur, a pioneer in university-level computer science education in Europe. 

Abstract Predictive maintenance (PdM) is increasingly being used to improve system reliability, reduce downtime, and optimize operating costs in complex industrial environments through anomaly detection. As industrial systems become more complex, with components interacting with one another and monitored by ever-expanding sensor networks, anomaly detection faces new methodological challenges. In particular, there is a gap in the literature regarding anomaly detection using traditional machine learning (ML) methods based on a Single Label Classification (SLC) approach, which is unsuitable when multiple failures can occur simultaneously.Traditional anomaly detection approaches based on ML methods using an SLC formulation assume that each observation belongs to a single anomaly category. This assumption does not hold when multiple failures occur simultaneously on the same equipment, resulting in a loss of information. We begin by illustrating this limitation through a concrete example showing that using an SLC approach instead of a multi-label formulation degrades the classification of simultaneous anomalies. To address this issue, anomaly detection is reformulated as a Multi-Label Classification (MLC) problem, enabling the detection of multiple failures within a single time step. Following this reformulation, the issue of selecting multi-label classifiers suited to the context of Predictive Maintenance (PdM) must be examined. The general conclusion from the literature is that the performance of classifiers depends heavily on the context and that while there are many detection methods, none is universally dominant. Although in-depth comparative studies exist in the field of MLC, datasets from complex systems are absent from these comparisons, even though they exhibit specific characteristics such as multivariate time-series structures, significant label imbalance, and complex interactions between components.To address this gap, a structured and reproducible evaluation protocol is proposed to evaluate eight state-of-the-art ML methods in an MLC framework across three public industrial datasets. The results confirm that the performance of classifiers depends heavily on the characteristics of the dataset and that no single method consistently outperforms others across all scenarios. However, conducting this type of comparison for each new industrial context is costly in terms of time, computational resources, and data requirements, which limits its feasibility for real-world industrial deployment. The thesis also addresses this issue through dimensionality reduction and variable selection methods. PM systems generate large multivariate datasets in which only a subset of the variables is actually relevant for anomaly detection. A self-adaptive evolutionary strategy is proposed to perform wrapper-style variable selection and obtain a subset containing only the most informative variables. Experimental results show that reducing the variable space improves computational efficiency and, in most cases, anomaly prediction performance. A comparison of the proposed method with state-of-the-art metaheuristic approaches on three PdM datasets and five anomaly detection methods confirms its competitiveness in terms of predictive performance and the optimal size of the selected feature subset. Finally, feature space reduction not only optimizes the resources required for detection but also reduces the number of signals that technicians must analyze during root cause investigations. This simplification aims to strengthen confidence in the system. Finally, the thesis addresses the practical aspects of deploying detection methods in complex systems. Among these, there is a lack of trust and transparency among maintenance technicians and decision-makers. An analysis of the interpretability and robustness of anomaly detection methods is provided to directly contribute to their deployment in real-world conditions. First, the proposed variable selection strategy is evaluated on a public PdM dataset using SHAP-based explanations to verify the consistency between the selected variables and those identified as the most important by tools dedicated to interpretability. The results show that the selected variables are consistent with those identified as the most important by these tools. Furthermore, visual analysis of the most influential variables reveals that anomalies are associated with a limited and well-defined subset of sensor signals, which helps improve transparency and strengthen confidence in automatic detection systems. At the same time, it is necessary to conduct a study of the robustness of multi-label classifiers in controlled sensor degradation scenarios, including different types of degradation and various levels of severity. The results show that the performance of classifiers can deteriorate significantly, particularly in the presence of gradual drifts. To mitigate this effect, introducing sensor degradation during training appears to be a relevant strategy. Finally, this thesis proposes a structured framework for multi-label anomaly detection in complex systems, covering classification methods, feature space optimization, interpretability, and robustness. For each of these aspects, the thesis adopts an industrial perspective and takes into account the differences between controlled laboratory experimental conditions and the additional constraints of real-world complex environments. The jury Prof. Katrien Beuls - University of Namur, BelgiumProf. Elio Tuci - University of Namur, BelgiumProf. Patrick Heymans - University of Namur, BelgiumProf. Jenni Raitoharju - University of Jyväskylä, FinlandMr. Fabio Pinna - Telespazio, BelgiumMr. Vito Trianni - ISTC-CNR, Italy Sign me up