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Supported by the SPW Research’s Win4SpinOff program and led by the University of Liège through its Mass Spectrometry Laboratory (MSLab, Faculty of Science) and the University of Namur through its Laboratory of Molecular Cancer Biology, (NARILIS, LBMC, Faculty of Medicine), the VENOM2 project explores the potential of peptides derived from animal venoms to develop new diagnostic and therapeutic solutions in oncology.

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.

A new study conducted by researchers at UNamur confirms a fact that is still largely unknown to the general public: not all combined oral contraceptives expose women to the same risk of venous thrombosis. This research was conducted by Lucie Raskin, a researcher at the University of Namur, under the supervision of Professor Jonathan Douxfils, a specialist in thrombosis and the effects of hormones on coagulation. This represents a significant advance in public health.

Research in the Service of Medicine.Thanks to research in the biomedical field, our life expectancy has increased significantly. Whether through genetic research or new methods for detecting and treating diseases, the biomedical sciences are contributing more than ever to advances in medicine. By studying biomedical sciences, you’ll explore the scientific aspects of disease to better diagnose and treat it, becoming indispensable partners in the medicine of tomorrow. 

Your health is your most valuable asset. To manage pain and illness, you regularly rely on medications. Natural, synthetic, biosynthetic—the choices are vast. The pharmacist, a medication specialist, is here to advise you. You’re familiar with the community pharmacist, but pharmacy graduates can pursue many other careers to help improve our health and quality of life. 

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  

The fight against disease is both a science and an art. Knowledge of the human body, diseases, and medications is the key to fighting disease. But the essence of the medical art is practiced through interaction with patients who seek a doctor who is thorough, with solid knowledge and sound reasoning, but also deeply warm and compassionate. 

In response to the growing shortage of general practitioners in rural areas, the University of Namur is launching a groundbreaking initiative to encourage future practitioners to explore these regions. This year, nine rural placement centers have been established in the provinces of Namur, Hainaut, and Luxembourg, enabling 26 students in the Master’s program specializing in general practice (a joint UNamur-UCLouvain degree) to complete an internship under conditions that facilitate their immersion. Designed to address barriers related to housing, transportation, and isolation, this pilot program has found a particularly promising first implementation in Chevetogne, in partnership with the Province of Namur and several municipalities.

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

Défense de thèse - Antoine Hubermont 04/09/2026 à 15h à l'auditoire PA02. 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.

September 15, 2025 A program for all09h00 | Welcome at Pedro Arrupe (Rue de Bruxelles, 67 - 5000 Namur).11h00 | Back-to-school celebration at Saint-Aubain Cathedral (Place Saint-Aubain - 5000 Namur) then welcome students by the Cercles. First year bachelor Monday, September 15 - 2pm-3pm - VAUBAN auditorium : Welcome address Introduction to the various departments of the faculty Bachelor of Medicine 3pm - VAUBAN auditorium : Information session for students of the 1st Bachelor of Medicine by the Director of the Department of Medicine, Pr. Grégoire Wieërs. Bachelor in Pharmaceutical Sciences 3pm - auditorium PA01 (Pedro Arrupe) : Information session for students of the 1st Bachelor in Pharmaceutical Sciences by the pedagogical coordinator in Pharmaceutical Sciences, Mr. Romain Siriez. Bachelor in Biomedical Sciences 3pm - auditorium PA02 (Pedro Arrupe) : Information session for students of the 1st Bachelor in Biomedical Sciences by the Department Director in Biomedical Sciences, Prof. Jean-Pierre Gillet. Attendance at these sessions is compulsory. A student guide will be available on the Bureau Virtuel de l'Etudiant (BVE). From (date to be confirmed), classes resume according to a specific timetable (the 1st quadrimester timetable will be posted at the valves on September 9). Practical work (TP/TD) does not start for 2 weeks, timetables and groups will be posted in due course.All information concerning course timetables, TP/TD, EAP configuration, exemption requests, registration for isolated courses can be found on the Webcampus. Via the MEDINFO course (search in "All courses")Course salesA course sales schedule will be available on your Virtual Student Office (VSO): bookstores will be open to all from the start of the school year. Deux sessions pour vous préparer au concours L’accès aux études de médecine est notamment conditionné au classement en ordre utile  lors du concours d’entrée unique pour l’ensemble des universités de la Fédération Wallonie-Bruxelles. Ces sessions sont ouvertes à tous, mais s'adressent en particulier aux élèves qui ont fait le choix d'un programme de sciences générales dans l'enseignement secondaire. Elles constituent une aide précieuse dans la préparation des différentes matières inscrites au programme du concours, mais nécessitent un réel investissement personnel.En savoir plus sur les cours préparatoires au concours d'entrée en médecine...Pharmacie et en Sciences biomédicalesMettez tous les atouts de votre côté !  Cours préparatoires en août 2026 (dates à confirmer).En savoir plus sur les horaires des différentes sessions et s'inscrire aux cours préparatoires... 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.