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A new platform dedicated to research in the humanities and social sciences (SHS) is being launched at UNamur. The aim? To offer SHS researchers methodological support tailored to their needs and strengthen SHS excellence at UNamur. This platform, SHS Impulse, will provide various services such as financial support for training, consultancy, access to resources, or co-financed software purchases.

For Bruno Dumas, computer science fits in with the principles of applied psychology Artificial intelligence (AI) is interfering in our professional as well as our private lives. It both seduces and worries us. On a global scale, it is at the heart of major strategic, societal or economic issues, still being debated in mid-February 2025, at the AI World Summit in Paris. But how can we, as users, avoid being subjected to it? How can we gain access to the necessary transparency of its workings? By placing his research prism on the user's side, Bruno Dumas is something of a "computer psychologist". An expert in human-computer interaction, co-president of the NaDI Institute (Namur Digital Institute), he defends the idea of a reasoned and enlightened use of emerging technologies.

A major breakthrough for the health of women on oral contraceptives has been achieved by researchers in Namur. Every year in Europe, more than 22,000 cases of venous thrombosis or embolism occur among contraceptive pill users. A team of researchers from the University of Namur, in collaboration with QUALIblood (spin-off), has conducted an important study, the results of which could transform prescribing recommendations..

Bringing a new drug to market, particularly when it is intended to treat a pediatric or orphan disease, is a journey fraught with pitfalls. The ERAMET project, coordinated by Professor Flora Musuamba Tshinanu, aims to overhaul the assessment procedures, so that regulatory agencies can take innovative technologies into account and thus make more informed choices.

Can a computer learn a language like a child? A recent study published in the leading journal Computational Linguistics by Professors Katrien Beuls (Université de Namur) and Paul Van Eecke (AI-lab, Vrije Universiteit Brussel) sheds new light on this question. The researchers argue for a fundamental revision of the way artificial intelligence acquires and processes language.

On May 10, 2025, Vice-Rector Carine Michiels and Professor Anne-Catherine Heuskin handed over UNamur's cheque at the grand closing evening of Operation Télévie, which this year raised a record €13,351,977 for the Fonds National de la Recherche Scientifique. Télévie funds are used entirely to finance cancer research projects at universities in the Wallonia-Brussels Federation and the Grand Duchy of Luxembourg.

This summer's third and final focus on the NaDI-CeRCLe research center, which has gained international recognition in recent weeks thanks to awards won by three young researchers in service management. Following on from Floriane Goosse and Victor Sluÿters, we invite you to discover the work of Florence Nizette, a young researcher working on Artificial Intelligence technologies.

Researchers at the Universities of Namur (UNamur), Brussels (ULB) and Liège (ULiège) have just taken a key step towards understanding viral mechanisms. Their study, published in the international scientific journal PLOS Pathogens, focuses on a particular type of molecule produced by viruses, circular RNAs, and presents an innovative bioinformatics tool capable of better identifying them.

A flurry of awards for the NaDI-CeRCLe research center in recent weeks. The service management research of three young doctoral students from the EMCP Faculty has been recognized by their peers at leading international scientific events: Floriane Goosse, Victor Sluÿters and Florence Nizette. This summer, we invite you to discover their careers and their work.

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

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

Hepatocellular carcinoma is the most common primary liver cancer. Unfortunately, this tumor still has a high mortality rate due to the lack of effective treatments for its most advanced or poorly localized forms. As part of a partnership with the CHU UCL Namur - site de Godinne and with the support of Roche Belgium, researchers in the Department of Biomedical Sciences are trying to understand why liver tumor cells are so resistant to treatment, and to identify therapeutic alternatives to better target them.