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As it does every year, the Faculty of Computer Science is organizing a job fair dedicated to digital professions. It's the UNamur IT Jobs Fair 2025, on Thursday 13/11 at the Arsenal.

As it does every year, the Faculty of Computer Science is organizing a job fair dedicated to digital professions. It's the UNamur IT Jobs Fair 2024, on Tuesday 12/11 at the Arsenal.

Depuis son inauguration en 2019, l’observatoire astronomique de l’UNamur a déployé un programme pédagogique et de médiation scientifique ouvert à toutes et à tous, avec l’ambition de faire découvrir les sciences par le prisme de l’observation des merveilles du ciel. 5 ans plus tard, le pari est réussi ! La petite équipe qui anime les lieux multiplie les collaborations et les activités proposées aux étudiantes et aux étudiants, aux écoles et au grand public. Cet automne, l’Observatoire astronomique célébrera l’empreinte durable qu’il a construite dans les yeux et les cœurs de son public en fêtant son 5e anniversaire. Une occasion de rassembler la communauté qui s’est développée autour de ses projets passés, présents et futurs ! The program 11am-4pm: Tours of the observatoryUNamur - Faculté des sciences12pm-6pm: Opportunity to visit the Stellar Scape exhibitionLe Pavillon de la Citadelle de Namur6:30pm: Academic session and receptionUNamur - Faculty of Science (S01) Requested registration via ticketweb: https://www.billetweb.fr/5-ans-de-lobservatoire-antoine-thomas

Each year, UNamur's Faculty of Computer Science awards the Prix Jean Fichefet for the best master's thesis in computer science. From the 2022-2023 year, there will be two awards, which are entitled: Best Thesis Award (General Impact) and (Societal Impact).

Contact Secretariat +32 (0)81 72 52 52 secretariat.info@unamur.be Student secretariat Baccalaureate and Master's students in daytime classes isabelle.daelman@unamur.be Students of the Certificate in Data Science, Master in Cybersecurity, Master in Computer Systems Architecture "Masi" in day classes amelie.notaro@unamur.be Students on shift work and BAGI master's students benjamine.lurquin@unamur.be Address Faculty of Computer ScienceRue Grandgagnage, 21B-5000 Namur

Understanding and combating abnormal protein secretion and combating the envelope of pathogenic bacteria: these are the two focuses of two new UNamur projects selected as part of the 7th FRFS-Welbio call for projects. Among the 22 applications selected, Alison Forrester, a qualified FNRS researcher, has been awarded a Starting Grant and Xavier De Bolle, Professor, has been awarded an Advanced Grant. They are both WEL Research Institute Investigators and members of the Namur Research Institute for Life Sciences (NARILIS).

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. Daily prices Tuesday, September 16, 2025For Block 1 (Local I02)* - Welcome session08H30: Presentation of the Dean/Vice-Dean (Anthony Cleve - Marie-Ange Remiche)09H00: Presentation of the IS (Cédric Aerts)09H20: Presentation of the pedagogical coordinator (Fanny Boraita)09H40: Presentation of the academic advisor (Géraldine Grandjean)For students in the first 60 credits of the bachelor's degree (only first-time students*)10H40 (Local I02): passport to the "mathematics" baccalaureate(Florence Henry)Attendance at these sessions is compulsory.For UES** and new Master's students (Local I33) - Welcome session14H00: Presentation of the Dean/Vice-Dean (Anthony Cleve - Marie-Ange Remiche)14H30: Presentation of the IS (Cédric Aerts)14H50: Presentation of the pedagogical coordinator (Fanny Boraita)15H10: Presentation of the academic advisor (Géraldine Grandjean)15H30: Presentation of CSLabs (Hugo Raskin)Wednesday, September 17, 2025For Block 1 (Local I02)*<13H00: Passport "lire et comprendre un texte universitaire" (Alexandre Libioul)Attendance at this session is compulsoryFor all students: Classes start (see schedule)Permanences PAE17/09, 10h40-11h40 for NON-primo-arrivants from bloc1: Configuration PAE (salle académique)17/09, 09h00-10h00 for bacheliers from bloc2 and 3: Configuration PAE (salle académique)18/09, 09h00-10h00 for masters: Configuration PAE (salle académique)* First-time students : Students enrolled for the first time in a computer science study program at UNamur, whether they are coming from secondary school, a high school, another university or enrolled in staggered-schedule courses. ** UES: Unités d'enseignement supplémentaires au master (année passerelle) Staggered timetable courses Bachelor and Master 60Saturday, September 13, 2025 - Classes startFor Primo-arrivant students (Block 1 and UES):08:30: Breakfast welcome (coffee, pastries) in the faculty hall.09h00: Presentations by the Vice-Dean, Madame Marie-Ange REMICHE, the Academic Advisor, Madame Géraldine GRANDJEAN, the IT Correspondent, Monsieur Cédric AERTS and the Secretary, Madame Benjamine LURQUIN. Auditorium I02 (first floor of the Faculty of Computer Science). Attendance is compulsory. The welcome session presentation will be posted on the BVE afterwards.10:00 am: Start of classes for all students. Master's specialization in IT and innovation: business analysis and it governance For students concerned by prerequisite coursesSaturday, September 13, 2025 at 9:00 am, seminar I22 on the 2nd floor of the Faculty.For all new studentsCourses start on Friday, October 17 and Saturday, October 18, 2024 at the Academic Hall on the 4th floor of the Faculty of Computer Science, from 8:30 am. And before school starts? In addition to the cpreparatory courses scheduled between mid-August and early September, the University of Namur is offering newcomer students the chance to discover their Faculty as well as the campus, and to take part in a services forum during 2 integration days.Exclusively aimed at students completing their secondary education (newcomers), these preparatory courses are tailored to each university program.Preparatory courses: from August 18 to 28, 2025 for computer science studentsFind out more about the schedules for the various sessions and register for the preparatory courses... NEW! To help you make the most of your first year at the University, take part in our integration days!Friday afternoon, September 12 - reserved for newcomers, free, registration requiredTour of your Faculty and campus (integrated into the preparatory courses if you are enrolled)Barbecue and evening party You must register for both activities, even if you are enrolled in the preparatory courses! The registration link will be available soon.Saturday, September 13, 10am-4pm - open to all - free, open accessServices forum: presentation of student services (sports, culture, commitment, social cell, ...), project kots and activities organized on campus...

The F.R.S.-FNRS published on June 25, 2024, the list of winners of the various doctoral and postdoctoral mandates. Among them, 16 researchers from the University of Namur have obtained funding.

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