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Research seminar co-organized by Louis Carré and Sabina Tortorella as part of the activities of the Esphin Institute, the Department of Philosophy, and the Arcadie Center as well as the Marie Skłodowska-Curie SOCIAL project This seminar sets out to explore contemporary uses of Hegel's practical thought as well as to question its relevance and legacy. Themes central to recent philosophical debates, such as globalization, race, feminism and the Anthropocene, as well as contemporary challenges facing philosophical reflection - such as social justice and ecological transition, state sovereignty in the face of international markets and the emergence of supranational subjects, or the crisis of democracy in the face of the rise of populism and the return of war - may call for a mobilization of Hegelian thought. The aim of this seminar is not necessarily to propose a strictly historical-philosophical reading of Hegelian thought, but rather to seek to take Hegel beyond Hegel himself, by engaging in a reflection on problematics that find their first formulation in him, but that have developed far beyond his conceptual framework, or by questioning his concepts from perspectives that do not necessarily lay claim to Hegelianism. The aim of this approach is to bring Hegel's thought into dialogue with other philosophical traditions and currents of political philosophy, in order to question its ability to shed light on some of the major issues of our time. By questioning its topicality and limitations, this seminar aims to examine what practical Hegelian philosophy can still offer us today, and how it enables us to question our own problems from a renewed angle. While it is unlikely to provide ready-made solutions, it can perhaps help us to ask the right questions and think differently about the tensions of our time.Chiara Magni (Università degli Studi Roma Tre) will speak on the theme: "What rights for the accused and the convicted? Criminal prosecution and human dignity in the light of Hegel's practical philosophy"Link to attend the online seminar Contact: sabina.tortorella@unamur.beThis project has received funding from the European Union's Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101150961.

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.

The NaDI-CeRCLe research center has distinguished itself brilliantly on the international scene in recent weeks. Three young researchers from the EMCP Faculty have received prestigious recognition at leading international events for their research in service management: they are Floriane Goosse, Victor Sluÿters and Florence Nizette. This summer, let's discover the work of these PhD students and their significant contributions to the advancement of knowledge and practice in this field.

A lifelong video game enthusiast, Fanny Barnabé, a researcher at the CRIDS research center (Namur Digital Institute) and lecturer at the University of Namur, explores behind the scenes of a major cultural phenomenon: video game streaming on Twitch. Between humor, irony and toxic discourse, she deciphers the issues at stake in a digital space in the throes of change.

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

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.

The notion of "academic freedom" is often evoked in public debates, but is it really known in its singularity? Where does it come from? What is its history? What decisive role does it play in our democracies and the defense of the university's role as a counter-power? To what extent is it threatened today, particularly by the reforms announced?This morning of reflection, open to all, will explore these questions from a variety of perspectives. Historians, philosophers and jurists will shed light on the subject. Institutional leaders will share their concrete experience and their analysis of the threats that reforms could pose to this essential principle.An organization of the Philosophy Department, the History Department, the ESPHIN Institute in collaboration with the Centre d'Action Laïque Namur.The speakers- Anne-Emmanuelle Bourgaux, Professor of Constitutional Law at the University of Mons- Nicolas Offenstadt, Professor of History at the University of Paris 1 Panthéon-Sorbonne- Quentin Landenne, Qualified Researcher at the FRS-FNRS at the UCLouvain Saint-Louis Brussels, scientific leader of the ERC BildungLearning project- Susanna Zellini, Research Fellow at the FRS-FNRS at UCLouvain Saint-Louis Brussels- Annick Castiaux, Rector of UNamur- Edouard Delruelle, Professor of political philosophy at ULiège; Chargé de mission by the Rectrice of ULiège on "ULiège facing global conflicts".PracticalitiesThéâtre Jardin Passion 39, rue Marie Henriette - 5000 Namur.Free admission - Welcome from 9amAn organization of the Departments of Philosophy and History of the University of Namur and the ESPHIN Institute in collaboration with the Centre d'Action Laïque Namur with the support of the ERC BildungLearning project. I want to register The ERC BildungLearning project is funded by the European Union (n° 101043433). The views and opinions expressed, however, are those of the authors alone and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

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

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.