Search results

At UNamur, a doctoral thesis led by Jozie Tientcheu—with support from SPW Recherche as part of the Win4Doc program—is exploring a new approach to speed up the diagnosis of bacterial antibiotic resistance. Called STABLE2, the project is being developed in collaboration with Coris BioConcept, a Walloon company specializing in rapid diagnostic tests. 

Organizing CommitteeMaxime Hilbert (University of Namur)Lucie Boël (Jean Moulin University of Lyon 3, IRPhiL)Alexandre Francq (University of Paris 1 Panthéon-Sorbonne, Gustave Roussy Institute, Montpellier Institute of Functional Genomics)Eve-Aline Dubois (University of Namur)Azat Garaev (Catholic University of Louvain)Frida Trotter (Independent Researcher)Doan Vu Duc (University of Namur)Victoria Van Gheem (Catholic University of Louvain)Scientific CommitteeChristine Clavien (University of Geneva, iEH2)Alexandre Guay (Catholic University of Louvain)Quentin Hiernaux (Free University of Brussels)Vincent Ardourel (University of Paris 1 Panthéon-Sorbonne, CNRS)Julie Jebeile (CNRM, CNRS)Baptiste Le Bihan (University of Geneva)Soazig Le Bihan (University of Montana)Matteo Mossio (University of Paris 1 Panthéon-Sorbonne)Olivier Sartenaer (University of Namur)Pieter Thyssen (University of Liège)Laurence Bouquiaux (University of Liège)Antonine Nicoglou (University of Tours) More info

The members of the jury are:Prof. Laura RIZZERIO (Chair), UNamur; Prof. Laurent RAVEZ (Advisor, Secretary), UNamur; Prof. Aude BANDINI, University of Montreal; Prof. Bertrand HESPEL, UNamur; Prof. Sami RICHA, Saint Joseph University of BeirutProf. Grégoire WIEËRS, UNamurThe announcement will be followed by a reception in the Academic Hall.

Practical information:💶 Admission: Free🧑 Target audience: All ages📅 Date: Friday, May 22🕙 Hours: 6:00 PM to 12:00 AM (continuous) (Except for Observatory tours by reservation)📍 Location: Cour de médecine - 3 Rue Joseph Grafé, NamurOn the programLe Confluent des Savoirs, UNamur’s research outreach and public engagement service, invites you to experience an exceptional evening at the heart of the university. Step inside spaces usually off-limits to the public and be amazed by the wealth of activities on offer.Explore the zoology collections and discover the animal world through fascinating specimens and observations. Then take to the skies with tours of the Antoine Thomas Astronomical Observatory—available by reservation only—for a deep dive into the mysteries of the heavens.At the Moretus Plantin University Library (BUMP), let yourself be swept up in a captivating scavenger hunt, following in the footsteps of two iconic figures from Namur folklore: a fun adventure blending puzzles and exploration.Also travel back in time by meeting researchers who reveal the secrets of medieval parchments. Between history and the exact sciences, discover how scientific analysis today makes it possible to identify the animal origin of these precious writing materials.Finally, delve into the heart of today’s environmental challenges with the exhibition “Sentinels in Troubled Waters.” This cross-border research project (ORION) highlights the study and modeling of water quality in the Meuse River basin in the face of human pressures and the challenges of climate change.A unique evening to explore, understand, and marvel… to the rhythm of science.Whether you’re curious, passionate, or simply looking for a unique experience, the Nuit Blanche at UNamur promises a journey rich in discoveries, accessible to all. 

Abstract Deep learning has become a central part of everyday life, and has given us powerful digital tools such as machine translators, voice assistants, and large language models. The Deaf communities, however, benefit verylittle from this progress. Sign languages are full natural languages, but they have no widely used written form, so the large text corpora and pretrained models that support these tools for spoken languages have no direct equivalent. As a result, sign language processing is held back by two related problems: a persistent shortage of annotated data, and a lack of tools to analyze the languages themselves.This thesis focuses on one component of the sign language processing pipeline that has received comparatively little attention: sign language segmentation, the temporal partitioning of a continuous signing video into individual sign units. Segmentation sits between raw video and almost every symbolic task built on top of it, yet it remains poorly understood. The goal of this work is twofold: to advance the segmentation task itself, and to turn sign language processing research into tools that are usable by the Deaf community, with French Belgian Sign Language (LSFB) as the main setting.The central contribution of this thesis is a set of segmentation models. We first study approaches based on recurrent neural networks, and show that their main difficulty lies in the modeling of the transitions between signs, the brief ambiguous movements that separate one sign from the next and that are easily confused with the signs themselves. This insight motivates the Hydra framework, a new approach that detects each sign as a whole unit rather than deciding, frame by frame, whether a sign is being produced. Concretely, instead of classifying every frame, the model predicts the distance from each frame to the nearest sign boundaries, which makes it less sensitive to the ambiguous regions and improves consistently over prior methods. Around this core, we present further contributions: a sign language-to-text dictionary that recognizes signs from an ordinary webcam, supporting work on isolated sign recognition, and a collaborative platform that gathers new sign data as a side effect of everyday use. Together, these contributions narrow the gap between sign language processing research and the people whose language it concerns. Jury Prof. Benoit Frénay - University of Namur, BelgiumProf. Anthony Cleve - University of Namur, BelgiumProf. Katrien Beuls - University of Namur, BelgiumProf. Barbara Hammer - Bielefeld University, GermanyProf. Michèle Gouiffès - Paris-Saclay University, FranceDr. Mathieu De Coster - Ghent University, Belgium I am registering to attend Pierre Poitier's thesis defense

September 14, 2026 What's on the schedule for everyone9:00 a.m. | Welcome at Pedro Arrupe, Rue de Bruxelles 67, 5000 Namur9:30 a.m. | Ceremony at Pedro Arrupe 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. Today's Rates September 15For Block 1 (Room I02)* - Welcome Session10:40 a.m.: Introduction by the Dean/Associate Dean (Anthony Cleve - Marie-Ange Remiche)11:10 a.m.: Introduction by the academic advisor (Géraldine Grandjean)11:30 a.m.: Introduction by the Academic Coordinator (Fanny Boraita)11:50 a.m.: Introduction to the Student Services Office (Cédric Aerts)12:10 p.m.: Introduction to CSLabs (Hugo Raskin) For students in the first 60 credits of the bachelor’s program (first-year students only*) – Room I022:00 p.m.: Introductory session for the English courseAttendance at these sessions is mandatory.For UES** students and new Master’s students (Room I30) – Welcome session2:00 p.m.: Introduction by the Dean/Associate Dean (Anthony Cleve – Marie-Ange Remiche)2:30 p.m.: Introduction by the academic advisor (Géraldine Grandjean)2:50 p.m.: Introduction by the Academic Coordinator (Fanny Boraita)3:10 p.m.: Introduction to the Student Services Office (Cédric Aerts)3:30 p.m.: Introduction to CSLabs (Hugo Raskin) September 16 For Block 1 (Room to be determined—check the schedule)*8:30 a.m.: Mathematical Foundations for Computer Science course (M. De Vleeschouwer)Attendance at this session is mandatoryFor all students: Classes begin (see schedule)Information session and PAE setupSeptember 18: Bachelor’s Program, Block 1 (non-first-year students): 1:00–2:00 p.m. – Academic HallSeptember 17: Bachelor’s Program, Blocks 2 and 3: 1:00–2:00 p.m. – Academic HallSeptember 16: MA1 and UES (non-first-year students): 1:00–2:00 p.m. in I30* First-time students: Students enrolled for the first time in a computer science program at UNamur, whether they are coming from high school, a college, another university, or are enrolled in a staggered-schedule program. ** UES: Additional course units for the master’s program (bridge year) Staggered-Schedule Classes Bachelor's and Master's 60Saturday, September 12—Classes BeginFor First-Year Students (Block 1 and UES):9:00 a.m.: Presentations in Room I02 by the Associate Dean, Ms. Marie-Ange REMICHE; the academic advisor, Ms. Géraldine GRANDJEAN; and the IT coordinator, Mr. Cédric AERTS. Lecture Hall I02 (ground floor of the School of Computer Science). Attendance at this session is mandatory. The presentation from the orientation session will be posted on the BVE afterward.10:00 a.m.: Classes begin for all students Specialized Master's Degree in Computer Science and Innovation: Business Analysis and IT Governance For students who need to take prerequisite coursesFriday, September 18, at 9:00 a.m., Seminar Room I22 on the 2nd floor of the Faculty building.For all new studentsClasses begin on Saturday, October 16, in the academic hall on the 4th floor of the School of Computer Science, starting at 8:30 a.m. 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.   

Catherine Linard, a researcher in the Department of Geography at UNamur, was recognized for her work at the intersection of geography and epidemiology. This Francqui-Collen fellowship will allow her, starting in September and for the next three years, to be relieved of most of her teaching duties so she can devote more time to her research on the influence of environmental changes on the spread of infectious diseases. 

AbstractThe development of efficient heterogeneous acid catalysts is essential for the sustainable valorization of biomass-derived platform molecules. This Ph.D. thesis focuses on the design, synthesis, characterization, and catalytic evaluation of novel acidic materials for two representative biomass upgrading reactions: the ketalization of glycerol to solketal and the conversion of ethyl levulinate to γ-valerolactone. Several classes of catalysts were investigated, including Group IV metal-doped mesoporous silica nanotubes and hollow nanospheres, sulfonic acid-functionalized silica materials, and porous metal phosphonates. The aim was to establish relationships between the properties of the catalysts—in particular their acidity—and their catalytic performance.The results demonstrated that the nature of the metal cation in metal-doped nanostructured silica strongly influences catalyst acidity and reactivity. Furthermore, synthesis parameters such as the loading of the metal cation and the preparation method were shown to control the Lewis/Brønsted ratio and the strength of Lewis acid sites, allowing the tuning of catalytic performance. Materials rich in Lewis acidity preferentially promoted the conversion of ethyl levulinate, while catalysts with higher Brønsted acidity were more effective in glycerol ketalization.The introduction of sulfonic acid groups significantly enhanced Brønsted acidity and led to extremely active catalysts for solketal production. At the same time, studies on layered phosphonates highlighted the crucial role of the phosphoric spacer in ensuring material stability and enabling their reuse over multiple catalytic cycles. For amorphous porous metal phosphonates, key synthetic parameters—including acid concentration, solvent choice, and the use of a templating agent—were found to significantly influence the acidity of the materials and, consequently, their catalytic activity.Overall, this work provides valuable insights into structure-acidity-reactivity relationships and offers guidelines for the rational design of heterogeneous acid catalysts for biomass valorization.JuryProf. Jérémy DEHON (UNamur), ChairProf. Carmela APRILE (UNamur), SecretaryProf. Sophie HERMANS (UCLouvain)Prof. Damien DEBECKER (UCLouvain)Prof. Vera MEYNEN (UAntwerpen)Prof. Tatjana PARAC-VOGT (KULeuven)

How can anomaly detection on a production line be improved? It was in response to a very concrete problem encountered at the company where he works that Arnaud Bougaham, with the support of his employer, decided to devote his thesis to this topic at the UNamur School of Computer Science. The goal? To develop an artificial intelligence model that assists operators in detecting anomalies on an industrial production line by reducing false alarms and making the diagnosis easier to understand. This approach also holds promise for deployment in the medical field. 

The program includes presentations by experts on Raman microscopy and its applications, hands-on sessions, and a tour of the facilities.Participants will have the opportunity to interact with specialists from ST Instruments and the Lasers, Optics & Spectroscopies platform during hands-on workshops and networking sessions.This event allows doctoral students to earn 1 ECTS credit; a certificate of participation can also be issued upon request.The morning will conclude with a networking lunch, and the afternoon will end with drinks. This event is free, but registration is required.  Sign me up Download the programme

Registration Form 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 ) Si vignette, numéro de plaque minéralogique ( 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.

Abstract  The design of a content-aware urban traffic management and monitoring system based on a swarm of drones is a challenging problem due to real-time data processing, multi-agent coordination, and decision-making under dynamic conditions. The complexity of this design problem increases with the number of options and the number of cues that traffic controllers must consider when making a decision. The research presented in this thesis addresses this challenge by exploring the potential of swarm intelligence and reinforcement learning-based controllers as design tools for developing adaptive, content-aware decision-making mechanisms for urban traffic management and monitoring using a swarm of drones. The main objective is to design distributed control mechanisms that support the emergence of robust, scalable, and effective autonomous traffic monitoring and management, while proposing and evaluating alternative solutions compared to previous research. In particular, we propose alternative approaches to elements such as the use of predefined correlations between traffic dynamics and driving characteristics, as well as the use of fixed monitoring mechanisms—such as cameras or radar sensors—combined with fixed control strategies.With regard to traffic monitoring, this thesis focuses on the evaluation of swarms of drones for traffic data collection. With regard to traffic management, this thesis focuses on the evaluation of two traffic control methodologies applied to three different urban scenarios. The control methodologies are: the centralized Simultaneous Perturbation Stochastic Approximation (SPSA) and the decentralized Deep Q-Network (DQN). Briefly, in the former method, the agents operating on traffic collectively evaluate the effects of their actions on traffic quality to update their decision-making policy. The latter method requires agents managing traffic to collectively select the best action from among several alternatives based on the estimated quality of those actions.In the centralized SPSA controller, we investigate both single- and multi-agent intersection maps using a cell transition traffic model (CTM) to simulate traffic. The experimental results indicate that the proposed drone-based traffic controller can successfully mitigate traffic jams and coordinate the swarm of drones, despite constraints on individual exploration and limited communications at a single urban intersection. However, in multi-urban intersections, reducing traffic in one direction inevitably creates traffic problems in other parts of the road network due to a lack of coordination among agents managing traffic. The study also highlights the trade-off between adjacent intersections in the traffic pattern, where limiting communication can improve the swarm’s adaptability to changes. The evolved optimization controllers outperform traditional mechanisms—such as fixed traffic timing—in terms of accuracy, speed of convergence, varying vehicle input rates, and variations in the traffic model.In the second scenario, we first evaluate the performance of a centralized SPSA controller under realistic traffic dynamics using the Vissim simulator to simulate road traffic, rather than the CTM model. The aim is to evaluate the controller’s robustness against external disturbances arising from heterogeneous driving behavior. Next, we design and analyze a decentralized DQN controller for a multi-agent intersection map using the Vissim simulator. In this scenario, we extend the investigation to a more complex task in which drones must collectively transfer the collected data between intersections, and each agent selects its timing action based on both its own traffic data and information shared by other agents. The evolved drone-based DQN controllers enable the traffic units to learn about perceptual driving behavior and external disturbances from the Vissim traffic model. The results demonstrate the robustness and scalability of the evolved strategy under various conditions, including different initial conditions and driving behaviors. The swarm of drones also demonstrates adaptability to changes in traffic conditions, although this adaptability depends on the specific nature of the traffic dynamics and the information shared by other agents in the decision-making process.In the final scenario, we investigate multi-intersection traffic control on a simulated real map of the Belgian city of Namur, using a decentralized Deep Q-Network controller for traffic management and a swarm of drones for monitoring. The traffic model incorporates realistic features such as vehicle acceleration and deceleration, lane-changing behavior, and heterogeneous vehicle types. External disturbances, including parking events and pedestrian crossings, are also considered. The results demonstrate that the proposed autonomous traffic management and monitoring system is robust against disturbances and scalable to real urban areas. The study also highlights the effect of using a swarm of drones for data monitoring, compared to the same control strategy employing fixed cameras, in terms of convergence performance.In conclusion, this thesis contributes to the field of intelligent traffic management and monitoring by evaluating the potential of a swarm of drones as an effective tool for designing adaptive and coordinated traffic control and monitoring mechanisms. In the scenario under consideration, the results indicate that drone-based traffic controllers outperform conventional fixed-time traffic control and fixed-camera monitoring systems in terms of robustness, scalability, and adaptability. The findings of this research have implications for developing more resilient and autonomous drone-based traffic management systems capable of making informed collective decisions in complex and dynamic urban environments. Jury Prof. Elio Tuci - University of Namur, BelgiumProf. Laurent Schumacher - University of Namur, BelgiumProf. Marie-Ange Remiche - University of Namur, BelgiumProf. Alexandre Mauroy - University of Namur, BelgiumProf. Seyed Amir Tafrishi - University of Cardiff, UKProf. Ahmad Fakharian - Islamic Azad University, Qazvin, Iran Sign me up