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Abstract Deep learning has become an extremely important technology in numerous domains such as computer vision, natural language processing, and autonomous systems. As neural networks grow in size and complexity to meet the demands of these applications, the cost of designing and training efficient models continues to rise in computation and energy consumption. Neural Architecture Search (NAS) has emerged as a promising solution to automate the design of performant neural networks. However, conventional NAS methods often require evaluating thousands of architectures, making them extremely resource-intensive and environmentally costly.This thesis introduces a novel, energy-aware NAS pipeline that operates at the intersection of Software Engineering and Machine Learning. We present CNNGen, a domain-specific generator for convolutional architectures, combined with performance and energy predictors to drastically reduce the number of architectures that need full training. These predictors are integrated into a multi-objective genetic algorithm (NSGA-II), enabling an efficient search for architectures that balance accuracy and energy consumption.Our approach explores a variety of prediction strategies, including sequence-based models, image-based representations, and deep metric learning, to estimate model quality from partial or symbolic representations. We validate our framework across three benchmark datasets, CIFAR-10, CIFAR-100, and Fashion-MNIST, demonstrating that it can produce results comparable to state-of-the-art architectures with significantly lower computational cost. By reducing the environmental footprint of NAS while maintaining high performance, this work contributes to the growing field of Green AI and highlights the value of predictive modelling in scalable and sustainable deep learning workflows. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Gilles Perrouin - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Clément Quinton - University of Lille, FranceProf. Paul Temple- University of Rennes, FranceProf. Schin'ichi Satoh - National Institute of Informatics, Japan

Researchers, graduate students, artists, designers, teachers, and creatives from all walks of life: this is a unique opportunity to take your ideas beyond the usual framework and bring them to life in a new way. The goal of this first edition: to collaboratively design an educational treasure hunt in Namur, centered on the theme of misinformation, aimed at young people aged 15 to 18.Practical detailsWhen? June 1, 2, and/or 3, 2026 (9 a.m.–4 p.m.), flexible participation (a few hours, half a day, a full day, or all three days)Where? The TRAKK in NamurLunch provided by NADI + unlimited coffee/tea in a collaborativeatmosphere Everything is set up for experimenting, testing, and creatingRegistration: free, but please send an email to digifactory.asbl@gmail.comA chance to bridge disciplines, break out of silos…and invent new ways to tell stories, convey information, and share.Note: For doctoral students, participation counts toward your doctoral program (course credits).

An innovative educational initiative was carried out at the University of Namur during the 2024–2025 academic year as part of the “Software Testing and Quality” course in the Master’s program in Computer Science, with a focus on software engineering. This initiative led to the publication of the SNAIL Report 2025, a comprehensive barometer of software development practices in the Wallonia-Brussels Federation.

Increasingly sophisticated technologies are invading our spheres of activity without our prior consultation as citizens. Shouldn't the new digital tools, artificial intelligence or technologies resulting from progress in neuroscience, which are transforming our identity and social relationships, be the subject of broad and sufficiently informed democratic debates? This question is at the heart of the international "research-action" project "A new humanism in the age of neuroscience and artificial intelligence" in which UNamur is participating.

Japan is almost 10,000 kilometers from Belgium, a country that fascinates, not least for its rich culture full of contrasts. Researchers at UNamur maintain close ties with several Japanese institutions, particularly in the fields of computer science, mathematics and video games. Let's take a look at some of these collaborations..

Anthony Simonofski and Olivier Sartenaer, have been elected to join the prestigious Collégium de l'Académie royale de Belgique. Bringing together young researchers (under 40) from Wallonia-Brussels who have particularly distinguished themselves in their careers, the Collégium's objectives include promoting the arts and research.

Space has become a major economic and strategic issue. As a member of the European UNIVERSEH Alliance, UNamur explores this space theme in its various departments, from physics to geology, via mathematics, computer science or philosophy. Without forgetting to address the general public, who still dream of the stars...

The F.R.S.-FNRS has just published the results of its various 2025 calls for proposals. These include the "Credits & Projects" and "WelCHANGE" calls, as well as the "FRIA" (Fund for Research Training in Industry and Agriculture) and "FRESH" (Fund for Research in the Humanities) calls, which aim to support doctoral theses. What are the results for UNamur? Twenty-eight projects have been selected, demonstrating the quality and richness of research at UNamur. 

Our keynote speakers for 2026 are Professor Roosmarijn Vandenbroucke (Ghent University) and Professor Nelly Litvak (Eindhoven University of Technology). More information on the "Women in Science" website

Flamure Ibrahimi is a Ph.D. student in service and marketing management at the NaDI-CeRCLe Research Center at the University of Namur (Belgium) within the EMCP Faculty, under the supervision of Prof. Dr. Wafa Hammedi (University of Namur) and Prof. Dr. Linda Alkire (Texas State University). She has just been awarded the prestigious ServCollab Scholarship 2026, an international distinction that recognizes and supports doctoral students whose work falls within the field of Transformative Service Research (TSR)—doctoral projects with high impact on society and humanity.

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

Sara Belghiti, a doctoral student at the University of Namur (NaDI-CeRCLe Institute) and a teaching and research assistant at the IÉSEG School of Management (France), has just been recognized by the AMS Review – Sheth Foundation Doctoral Competition for Conceptual Articles (DoCCA) 2026, one of the world’s most prestigious doctoral competitions in theoretical and conceptual marketing and management.