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A team of American researchers, with the help of Frédérik De Laender, professor in the Department of Biology at UNamur, has just published in the prestigious journal Nature. Their study describes how changing stream temperatures and human introductions of fish can alter river biodiversity in the USA.

At the beginning of each academic year, the Board of Trustees grants Namur Research College (NARC) Fellowship status to researchers who demonstrate a high level of research achievement and who have recently received a prestigious award or funding. A look back at the fellowship of Professor Frederik De Laender.

The F.R.S.-FNRS has just published the results of its various 2024 calls. Equipment calls, research credits and projects, FRIA doctoral grants and Mandant d'Impulsion Scientifique (MIS), there are many instruments to support fundamental research. Find out more about UNamur's results.

Jamie Wright, a researcher at the Namur Digital Institute at UNamur, participated in a study conducted by Pompeu Fabra University (UPF) in Barcelona and published in the prestigious journal *Nature Human Behaviour*.  The study shows that languages around the world tend towards lexical economy, reusing words to denote different concepts when doing so does not lead to communicative confusion.

The result of a collaboration with Isabelle Frochot of the University of Burgundy Europe, a study in which Alain Decrop, a marketing professor at the EMCP Faculty, participated, shows that the mountains foster a genuine reconnection with oneself, with others, and with one’s environment. The results of this research were published in the prestigious journal *Recherche et Applications en Marketing* (RAM).

Detecting a failure before it occurs: that is the goal of the research being conducted by Antoine Hubermont, a doctoral student at UNamur. This project, named Monsater, is funded by SPW Research as part of the Win4Doc program in collaboration with the space company Telespazio Belgium. It addresses a key strategic challenge: ensuring the reliability of complex systems, particularly in the space sector. 

JuryProf. Timoteo CARLETTI (UNamur), ChairProf. Anne-Sophie LIBERT (UNamur), secretaryProf. Benoît FRENAY (UNamur)Prof. Ugo LOCATELLI (University of Rome Tor Vergata)Prof. Konstantin BATYGIN (California Institute of Technology)Prof. Adrien LELEU (University of Geneva)AbstractThe growing number of extrasolar systems detected over the past three decades has made it necessary to develop fast and reliable methods for studying the long-term stability of planetary systems. In this work, we address the complex problem of the stability of compact three-planet systems—in which resonant and chaotic behaviors are intrinsically linked—using chaos indicators and machine learning. In the first part, we design four (variational and non-variational) chaos indicators and test their performance on a synthetic dataset. In the second part, we examine the predictive power of the chaos indicators when combined with different machine learning strategies. Finally, we apply these methods to a synthetic population generated by the Bern formation model, providing results on the long-term behavior and dynamical characterization of the multiple-planet systems in this population. Our analysis highlights the effectiveness of the dynamical tools developed here for assessing the stability of near-resonant systems and paves the way for their use in future space missions.

Through research, teaching, and knowledge sharing, Wafa Hammedi, a professor at Sciences Po’s Faculty of Economics, Management, and Communication (EMCP), has charted a path guided by her convictions. Far from the performance-driven clichés typically associated with marketing, she advocates for socially engaged research that serves society, equity, and inclusion.

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

Researchers at UNamur have developed a new method that has uncovered 32 previously undetected bugs in some of the most widely used software libraries for training artificial intelligence models. Originating from a master’s thesis, this research has already led to fixes in several software programs used by millions of developers around the world. 

The days when people chose a career once and for all and when experience alone was enough to climb the career ladder seem to be over. Nowadays, lifelong learning has almost become the norm. “There is clearly a trend in this direction, particularly in the context of automation and artificial intelligence, which makes acquiring new skills all the more important,” says Michel Ajzen, a professor in the Management Department at the Faculty of Economics, Management, and Communication at SciencePo, UNamur, and a member of the Namur Digital Institute. “And it’s not a generational issue: whether you’re 30, 40, or 50, this need exists.” 

Through five interdisciplinary lecture-workshops, doctoral students from UNamur NaDI invite the general public to develop a critical perspective on artificial intelligence, which is already transforming our daily lives.