Projets
Équipe : ALMASTY
Résistance des systèmes cryptographiques
Le projet Cryptanalyse s’intéresse à l’étude et à la standardisation des primitives cryptographiques. En effet la cryptographie moderne est devenue un outil indispensable pour sécuriser les communications personnelles, commerciales et institutionnelles. Ce projet permettra de fournir, une estimation des difficultés de résoudre les problèmes sous-jacents et d’en déduire le niveau de sécurité que confère l’utilisation de ces primitives.
La problématique est l’évaluation de la sécurité des algorithmes cryptographique.
Project Leader : Charles Bouillaguet
01/10/2023
Tailles de clés : optimisations pratiques et théoriques et approches modernes pour des estimations précises du coût de NFS
Project Leader : Charles Bouillaguet
01/10/2021
Calcul réparti sécurisé : Cryptographie, Combinatoire, Calcul Formel
Project Leader : Damien Vergnaud
10/09/2021
Équipe : ALSOC
Vérification formelle et résilience aux attaques physiques de contre-mesures matérielles
Project Leader : Emmanuelle Encrenaz
08/04/2025
Modélisation et Vérification pour CPS Sécurisés et Performants
Project Leader : Daniela Genius
01/10/2023
Conception de systèmes sécurisés par une réduction des effets de la micro-architecture sur les attaques par canaux auxiliaires
Project Leader : Quentin Meunier
01/10/2020
TSAR - TSAR (Tera-Scale ARchitecture)
Project Leader : Alain Greiner
27/09/2017
Équipe : APR
Analyse de paramètres de classes de DAGS
Project Leader : Antoine Genitrini
01/10/2023
Leveraging Software Heritage to Enhance Cybersecurity
Project Leader : Antoine Mine
01/10/2023
Raisonnements formellement certifiés en apprentissage automatique
Project Leader : Antoine Mine
01/10/2023
Improving Digital Systems Security Evaluation
Project Leader : Antoine Mine
01/07/2022
TORI - In-situ Topological Reduction of Scientific 3D Data
TORI (In-situ Topological Reduction of Scientific 3D Data) is an ERC Consolidator research project started in October 2020 and coordinated by Julien Tierny. It aims at addressing the explosion in size and complexity of large-scale data by developing the next generation data reduction tools based on topological data analysis.
Project Leader : Julien Tierny
01/10/2020
Équipe : BD
Enrichissement d'une base de connaissances à partir de données prosopographiques médiévales incertaines
Project Leader : Camelia Constantin
01/11/2025
https://scanr.enseignementsup-recherche.gouv.fr/projects/ANR-25-CE38-0700
experimaestro - Planification et gestion d'expériences informatiques
Experimaestro is an experiment manager based on a server that contains a job scheduler (job dependencies, locking mechanisms) and a framework to describe the experiments with JavaScript or in Java.
Project Leader : Benajmin PIWOWARSKI
01/01/2016
SPARQL on Spark - SPARQL query processing with Apache Spark
A common way to achieve scalability for processing SPARQL queries over large RDF data sets is to choose map-reduce frameworks like Hadoop or Spark. Processing complex SPARQL queries generating large join plans over distributed data partitions is a major challenge in these shared nothing architectures. In this article we are particularly interested in two representative distributed join algorithms, partitioned join and broadcast join, which are deployed in map-reduce frameworks for the evaluation of complex distributed graph pattern join plans. We compare five SPARQL graph pattern evaluation implementations on top of Apache Spark to illustrate the importance of cautiously choosing the physical data storage layer and of the possibility to use both join algorithms to take account of the existing predefined data partitionings. Our experimentations with different SPARQL benchmarks over real-world and synthetic workloads emphasize that hybrid join plans introduce more flexibility and often can achieve better performance than join plans using a single kind of join implementation.
Project Leader : Hubert NAACKE
01/01/2015
http://www-bd.lip6.fr/wiki/en/site/recherche/logiciels/sparqlwithspark
BOM - Block-o-Matic!
Block-o-Matic est un algorithme de segmentation de pages Web basé sur une approche hybride pour la segmentation de documents numérisés et la segmentation de contenu à base visuelle. Une page Web est associée à trois structures: l'arborescence DOM, la structure de contenu et la structure logique. L'arborescence DOM représente les éléments HTML d'une page, la structure géométrique organise le contenu en fonction d'une catégorie et de sa géométrie et enfin la structure logique est le résultat de la cartographie de la structure du contenu sur la base du sens humain. Le processus de segmentation est divisé en trois phases: l'analyse, la compréhension et la reconstruction d'une page Web. Une méthode d'évaluation est proposée afin d'effectuer l'évaluation des segmentations de pages Web sur la base d'une vérité de terrain de 400 pages classées en 16 catégories. Un ensemble de mesures est présenté en fonction des propriétés géométriques des blocs. Des résultats satisfaisants sont obtenus en comparaison avec d'autres algorithmes suivant la même approche.
Project Leader : Andrès SANOJA
01/01/2012
Équipe : CIAN
ARCHitectures based on unconventional accelerators for dependable/energY efficienT AI Systems
Artificial Intelligence (AI) can power autonomous vehicles, provide strategic advantages through large-scale data analytics, and enable intelligence gathering and surveillance through advanced computer vision, opening a wide range of defence applications.
Conventional Von Neumann architectures, despite their flexibility, are inefficient for AI workloads due to data duplication and data movement bottlenecks, which severely limit the efficiency in the rapid processing of large amount of information and streaming data needed by AI algorithms.
In edge computing devices, crucial for defense applications, this inefficiency is compounded by energy limitations.
To overcome these challenges, specialised hardware and programming paradigm shift are needed to accelerate AI workloads. With the end of Moore’s law and Dennard scaling, simply scaling up existing architectures is no longer viable. Novel architectures are needed to improve performance per Watt and bypass the efficiency limits imposed by the Von Neumann bottleneck.
ARCHYTAS aims to investigate unconventional AI accelerators that take advantage of novel technologies: optoelectronic-based accelerators, volatile and non-volatile processing-in-memory, and neuromorphic devices. These technologies promise to mitigate the Von Neumann bottleneck by integrating processing and memory. ARCHYTAS also explores the integration of CMOS-based systems with analogue accelerators, as well as new programming models to improve the programmability, performance portability, and productivity of these emerging parallel systems through a hardware-AI co-design approach. The technological ambition of ARCHYTAS is to bridge the gaps in multi-modal sensing integration and AI processing, providing solutions that fit the non-functional requirements of future autonomous vehicles for defense applications.
The ARCHYTAS AI accelerators will be validated within the context of defense AI use cases in land, aerial, maritime, and space settings.
Project Leader : Haralampos Stratigopoulos
01/12/2024
Systèmes Bio-inspirés distribués de confiance : bases théoriques et mise en œuvre matérielle
Project Leader : Haralampos Stratigopoulos
01/10/2023
Trusted SMEs for Sustainable Growth of Europeans Economical Backbone to Strengthen the Digital Sovereignty
The internet of things (IoT) is promising as it drives the datafication of our everyday life and thus, leverages synergies between originally considered “dead” things and enables them to proactively serve humans. IoT leads to a high automation potential with which we improve the life of billions of people and compensate for societal problems such as a growingly old population, missing high-skilled labour across Europe or the efficiency limits in current production capabilities. IoT5.0, an Artificial Intelligence (AI) -assisted Internet of Things, could even more benefit society, as the devices could even learn how to provide more value. But the ubiquitous connectivity comes at a cost. Security levels have to rise tremendously to ensure a network stays secure and safe for humans. This additional effort often is a burden for small and medium sized enterprises as the complexity and security demands of such systems rise faster than available resources. This is especially dangerous as a single corrupted, malicious device can result in the exploitation of the entire network of connected devices by an attacker. Consequently, RESILIENT TRUST focuses on end-to-end security of IoT processing chains with a focus on strong exploitation for SMEs. This vision will be realized by developing specialized hardware to establish TRUST in-between a network and a wall of RESILIENCE even against new attack methods such as post quantum attacks and AI based attacks. The architecture of the secure processing chain will be carefully built after threat modelling, asset identification, risk analysis, security objectives and requirements definition. Consequently, RESILIENT TRUST will address and significantly mitigate these major risks to enable IoT5.0. That way this project will be a driver for sustainable development and the generation of convenience and wealth. A solution is proposed to ensure end-to-end security by boosting RESILIENCE and TRUST along different key supply chains of IoT device
Project Leader : Haralampos Stratigopoulos
01/10/2023
A network of excellence for distributed, trustworthy, efficient and scalable AI at the Edge
The vision of dAIEDGE Network of Excellence (NoE) is to strengthen and support the development of the dynamic European edge and distributed Artificial Intelligence (AI) ecosystem as an essential ingredient in the growth and competitiveness of European industrial sectors. The dAIEDGE Network aims to reinforce the research and innovation value chains to accelerate the digital and green transitions through advanced edge AI technologies, applications, and innovations, building on Europe's existing assets and industrial strengths. In parallel, it will fortify the edge AI research and industrial communities through technological developments beyond state of the art and become a dependable and strategic pillar for the European AI Lighthouse. This will be achieved by mobilising and connecting the European AI and edge AI constituency, the relevant stakeholders, European partnerships, and projects, to provide roadmaps, guidelines and trends supporting the next-generation edge AI technologies. The key aim is to support and ensure rapid development, market uptake and open strategic sovereignty for Europe in the critical technologies for distributed edge AI (hardware, software, frameworks, tools). The dAIEDGE NoE will play a catalyst role in building a solid edge AI virtual network of research facilities and laboratories to benefit the European research and industrial community. The NoE multidisciplinary concept provide an arena for matchmaking, exchanging ideas, tools, and services, by bringing together the leading research centres, AI-on-demand platforms, digital innovation hubs, AI projects and initiatives. The ultimate goal for the dAIEDGE NoE is to support Europe to become a global centre of excellence with unique human-centred edge AI competence addressing the social and economic challenges and the needs of the citizens and society.
Project Leader : Haralampos Stratigopoulos
01/09/2023
Compréhension et atténuation d’erreur dans les implémentations analogiques de réseaux de neurones sur silicium
Project Leader : Haralampos Stratigopoulos
01/10/2022
Récupération d'énergie mécanique proche des limites physiques par synthèse adiabatique de la dynamique électromécanique
C23/0800
Project Leader : Dimitri Galayko
01/10/2022
Architectures matérielles fiables pour l'Intelligence Artificielle de confiance
Project Leader : Haralampos Stratigopoulos
25/01/2022
CORIOLIS - Plate-forme pour la synthèse physique de circuits intégrés
Coriolis est une plate-forme logicielle pour la recherche d'algorithmes, le développement d'outils et l'évaluation de nouveaux flots de conception physique VLSI. Les procédés technologiques actuels, nanométriques, posent de nouveaux défis aux flots de CAO. Les recherches académiques concernent souvent la résolution de problèmes trop spécifiques, indépendemment d'autres algorithmes, faute de pouvoir inter-opérer avec eux. Or il est capital de pouvoir évaluer les interactions entre les différents outils au sein d'un flot complet de conception. La plate-frome CORIOLIS, conçue en C++, est faite pour permettre l'inter-opérabilité des différents briques logicielles qui l'utilisent. Elle propose actuellement dessolutions aux problèmes de partitionnement, de placement et routage de circuits numériques, en technologie nanométrique.
Project Leader : Jean-Paul CHAPUT
01/01/2004
CAIRO - Circuits Analogiques Intégrés Réutilisables et Optimisés
L'objectif du projet CAIRO est de développer des méthodes et des outils autorisant une réutilisation des cellules analogiques et une capitalisation de connaissances du concepteur sous forme des cellules IP (Intellectual Property) portables d’une technologie à l'autre et d’un jeu de spécifications à l'autre. Le langage CAIRO+, ensemble de fonctions C++, est un langage de création d’IP analogiques permettant de structurer, de formaliser et d’automatiser en grande partie le flot de conception analogique. Il est utilisé pour créer une procédure appelée «générateur» pour une cellule analogique. A l'étape actuelle d'avancement du projet, la structure électrique de la cellule (i.e. le schéma électrique non dimensionné) est figée par le concepteur. Le générateur doit permettre un dimensionnement des composants de la cellule (définition de la taille des transistors, des capacités etc.) et de synthétiser le layout – le tout en fonction des spécifications de la cellule et des paramètres technologiques. L'écriture du générateur de la cellule est à la charge du concepteur, notamment, la partie qui concerne le dimensionnement électrique du circuit. Un des points forts du langage CAIRO+ est, sans doute, la possibilité de synthétiser le layout d'une manière quasi-automatique, à partir du schéma électrique dimensionné – la fonction de génération du layout fait partie des modules «natifs» du langage. De plus, le dimensionnement électrique peut prendre en compte les éléments parasites du layout (nous disons «peut», car tout dépend de la volonté du concepteur qui définit la procédure de dimensionnement). Dans ce cas, plusieurs cycles «dimensionnement de la cellule – synthèse du layout» peuvent être nécessaires. Un des pôles d'intérêt de ce groupe est la conception de modulateurs sigma-delta temps continu. Dans cette activité nous nous attachons à capitaliser l’effort de conception en développant des méthodes et des outils permettant une réutilisation des résultats. La structure complexe des modulateurs, incluant un grand nombre de cellules de fonctionnalité identique mais de spécifications différentes (telles que GmC, amplificateurs), offre un contexte approprié pour l’application de la méthodologie implémentée dans CAIRO+.
Project Leader : Marie-Minerve LOUËRAT
01/01/2004
Équipe : DECISION
Algorithmes pour la prise de décision et l'apprentissage des préférences en optimisation multi-objectifs
Project Leader : Nawal Benabbou
01/10/2024
aGrUM - a Graphical Unified Model
aGrUM est une librairie en C++ de manipulation de modèles graphiques. Son spectre est assez large puisqu'elle est conçue pour faire de l'apprentissage (de réseaux bayésiens par exemple), de la planification (FMPDs) ou bien encore de l'inférence (réseaux bayésiens, GAI, diagrammes d'influence).
Project Leader : Christophe GONZALES & Pierre-Henri WUILLEMIN
Équipe : DELYS
Algorithmes distribués frugaux au coeur des réseaux
Project Leader : Pierre Sens
01/10/2024
Vers des applications serverless correctes par construction
Project Leader : pierre sens
01/10/2024
https://scanr.enseignementsup-recherche.gouv.fr/projects/ANR-24-CE25-5598
Désagrégation virtualisée
Project Leader : Julien Sopena
01/09/2023
Un nouveau paradigme de donnée : Les données autonomes et intelligentes
Project Leader : Franck Petit
01/10/2022
Équipe : LFI
Pelvic neRves autOmatic Segmentation using hybrId Trustworthy AI
Project Leader : Isabelle Bloch
01/10/2025
Exploitation de modèles d'explications pour les algorithmes d'apprentissage profond
Project Leader : Christophe Marsala
01/10/2024
Histoire des agences d'images et vision par ordinateur
Project Leader : Isabelle Bloch
01/10/2024
Méthodes Avancées pour l'Assistance à la Gastro-endoscopie Interventionnelle Endoscopique
Project Leader : Isabelle Bloch
01/01/2024
Apprentissage de mesure de similarité pour le transfert analogique
Project Leader : Marie-Jeanne Lesot
01/10/2022
Premature Human Connectome Patterns: mapping the fetal brain development using extreme field MRI
Project Leader : Isabelle Bloch
01/10/2021
Équipe : MOCAH
PostGenAI - PAC 3.3
Le projet présenté poursuit deux objectifs principaux :
Développer des modèles d’apprentissage adaptatifs réutilisables et flexibles, capables de s’adapter à différents profils d’apprenants et systèmes, en s’appuyant sur une approche post-IA générative.
Accompagner l’intégration de l’IA générative en éducation à travers la formation, le partage de méthodes et des recommandations, afin d’évaluer son rôle dans la transformation et l’industrialisation de la formation.
L’IA générative permet également de remplacer certaines tâches peu créatives, libérant du temps pour les enseignants et favorisant leur créativité.
La recherche repose sur une démarche participative pour concevoir les systèmes adaptatifs, et sur l’observation des processus industriels pour analyser l’impact de l’IA générative. Enfin, plusieurs initiatives existent déjà dans ce domaine, notamment au laboratoire LIP6, qui a contribué au partage de données et de modèles éducatifs.
Project Leader : Vanda Luengo
01/01/2025
IA pour la personnalisation de rétroactions dans l'apprentissage de la pensée informatique par le jeu
La spasticité est un trouble moteur caractérisé par une hyperactivité musculaire provoqué par l’alétration de la conduction nerveuse. Le diagnostic de cette pathologie repose sur l’évaluation du degré de résistance du membre suite à un mouvement passif réalisé par le praticien et sert à déterminer le traitement à suivre. Cependant cette évaluation reste subjective et requiert de l’expérience de la pratique. Seul un entraînement sur patient réel permet de gagner de l’expérience. C’est dans ce contexte que le projet HASPA a pour but de développer un simulateur permettant de reproduire différents degrés de spasticité pour permettre aux jeunes praticiens de s’exercer avant de pratiquer leurs gestes sur patient.
Project Leader : Sebastien Lalle
Partenaires : Le consortium réunit dans ce projet pluridisciplinaire est composé de 6 laboratoires publics (AMPERE, CEA-List, CRNL, LBMC, LIP6 SYMME) et va chercher à réaliser un simulateur haptique proposant une formation adéquate et pertinente pour les futurs praticiens.
01/10/2024
Simulateur haptique pour l'apprentissage de la spasticité
La spasticité est un trouble moteur caractérisé par une hyperactivité musculaire provoqué par l’alétration de la conduction nerveuse. Le diagnostic de cette pathologie repose sur l’évaluation du degré de résistance du membre suite à un mouvement passif réalisé par le praticien et sert à déterminer le traitement à suivre. Cependant cette évaluation reste subjective et requiert de l’expérience de la pratique. Seul un entraînement sur patient réel permet de gagner de l’expérience. C’est dans ce contexte que le projet HASPA a pour but de développer un simulateur permettant de reproduire différents degrés de spasticité pour permettre aux jeunes praticiens de s’exercer avant de pratiquer leurs gestes sur patient.
Project Leader : Vanda Luengo
Partenaires : Le consortium réunit dans ce projet pluridisciplinaire est composé de 6 laboratoires publics (AMPERE, CEA-List, CRNL, LBMC, LIP6 SYMME) et va chercher à réaliser un simulateur haptique proposant une formation adéquate et pertinente pour les futurs praticiens.
01/10/2022
Adaptiv’Math - Adaptiv’Math
obtenu dans le cadre du Partenariat d'Innovation Intelligence Artificielle (P2IA) du ministère de l'éducation nationale et porté par la startup EvidenceB, implique des entreprises (Nathan, Daesign, Schoolab, Isograd, BlueFrog), deux laboratoires (LIP6 et Inria Bordeaux), l'APMEP (association des professeurs de mathématiques) ainsi que des chercheurs en psychologie cognitive (E. Sander) et en neurosciences (A. Knopf). Il vise à réaliser un assistant pédagogique pour les mathématiques du Cycle 2 (CP, CE1, CE2) s'appuyant sur des algorithmes d'IA et sur un ensemble d'exercices définis à partir d'avancées en sciences cognitives.
Nous travaillons sur une brique IA visant à proposer des regroupements d'élèves (textit{clustering}) appris sur l'ensemble des classes sur la base de critères de maîtrise de compétences en mathématiques. Ce textit{clustering} est ensuite appliqué classe par classe à intervalles réguliers pour proposer à l'enseignant un suivi de l'évolution de ses groupes d'élèves, afin de faciliter la mise en place de stratégies de pédagogie différenciée.
Project Leader : François Bouchet
01/10/2019
MAGAM - Multi-Aspect Generic Adaptation Model
MAGAM est un modèle générique basé sur un calcul matriciel permettant d'adapter des activités d'apprentissage selon plusieurs aspects (pédagogique, didactique, ludique, motivationnel, etc.).
Project Leader : Vanda LUENGO et Baptiste MONTERRAT
01/03/2016
LEA4PA - LEarning Analytics for Adaptation and Personnalisation
Le projetLEarning Analytics for Personalization and Adaptation a pour objectif de proposer, à destination des décideurs (ensei-gnants, apprenants, ..), des algorithmes et des visualisations permettant des analyses du com-portement de l’étudiant pour l’adaptation et la remédiation. Il s’applique à plusieurs niveaux d’enseignement. Pour le niveau collège, la recherche est menée dans le cadre d’une collaboration soutenue par la direction du numérique pour l’éducation (MEN). Dans ce contexte, nous proposons des analyses (descriptives et diagnostiques) des compétences des apprenants en algèbre, ainsi que des visualisations, l’objectif étant d’assister l’enseignant dans l’adaptation des activités[4].Pour l’enseignement supérieur, la recherche est menée en s’appuyant sur la plateforme LA-PAD développée par CAPSULE (centre d’innovation pédagogique de Sorbonne Université).Dans ce contexte, nous nous intéressons à comprendre les parcours des apprenants à partir de techniques d’analyse séquentielle et de règles d’association.
Project Leader : Vanda LUENGO et Amel YESSAD
01/01/2016
Équipe : MoVe
Réduire l'empreinte énergétique des logiciels grâce aux changements de comportement des utilisateurs
Project Leader : Adel Noureddine
01/09/2025
BCMCyPhy - Modèle à composants pour les systèmes de contrôle cyber-physiques
Ce projet de recherche et le logiciel associé visent à concevoir et implanter un modèle de programmation par composants pour les systèmes de contrôle cyber-physique. Il se greffe sur le projet BCM4Java dont il utilise les concepts de base et l'implantation des composants répartis en Java (10.000 lignes de code et de documentation à ce jour). Outre le fait d'intégrer des composants temps réel, ce projet étudie les architectures de contrôle (automatique) à base de composants, leur spécification utilisant les systèmes hybrides stochastiques et leur simulation utilisation des modèles de simulation suivant la norme DEVS. Une attention particulière est portée sur la composabilité parallèle entre les composants, leurs spécifications et leurs modèles de simulation individuels. Un sous-projet logiciel de BCMCyPhy propose une nouvelle implantation en Java de la norme DEVS pour la simulation modulaire des composants et de leurs assemblages (20.000 lignes de code et de documentation à ce jour)). Les simulateurs obtenus par composition entre simulateurs de chaque composant permettent ensuite de mettre au point, tester, vérifier et valider les applications. Ce logiciel a été utilisé par quelques dizaines d'étudiants et l'est toujours dans le cadre du cours de master 2 ALASCA depuis 2018.
Project Leader : Jacques MALENFANT
18/06/2019
PNML Framework
PNML Framework est l'implémentation prototype du standard ISO/IEC-15909 (partie 2), le format d'échange normalisé pour les réseaux de Petri. L'objectif principal de PNML est d'aboutir à l'interopérabilité des outils basés sur les réseaux de Petri. PNML Framework est conçue pour implémenter le standard au fur et à mesure de son élaboration, afin d'en mesurer la faisabilité et de servir de référecne pour es outils de la communauté. Il propose une API de manipulation permettant de créer, sauver, charger et parcourir des réseaux de Petri au format PNML.
Project Leader : Fabrice KORDON
01/04/2005
CPN-AMI
CPN-AMI est un environnement conçu sur FrameKit: une plate-forme logicielle d'intégration permettant un couplage rapide d'outils de provenance diverses. CPN-AMi est ainsi l'assemblage le plus complet d'outils de vérifications à partir de réseaux de Petri. Ces outils ont été développés dans le cadre des travaux de SRC ou par des partenaires universitaires (Université de Turin, Université d'Helsinki, Bell-Labs, Université de Munich, Université Humbolt à Berlin). CPN-AMI est composé d'un serveur d'outils et d'une interface utilisateur déporté à laquelle on se connecte.
Project Leader : Fabrice KORDON
01/12/1994
SPOT - Spot Produces Our Traces
SPOT (Spot Produces Our Traces) est une bibliothèque de model-checking facilement extensible. À la différence des model-checkers existants, dont le mode opératoire est immuable, SPOT fournit des briques que l'utilisateur peut combiner entre elles pour réaliser un model-checker répondant à ses propres besoins. Une telle modularité permet d'expérimenter facilement différentes combinaisons, et facilite le développement de nouveaux algorithmes. D'autre part, cette bibliothèque est centrée autour d'un type d'automates particulier permettant d'exprimer les propriétés à vérifier de façon plus compacte, qui n'a jamais été utilisé dans un outil jusqu'à présent.
Project Leader : Denis POITRENAUD
Équipe : NPA
Towards a comprehensive pan-African research infrastructure in Digital Sciences
The African Union's Science, Technology, and Innovation Strategy for Africa (STISA-2024) positions science, technology, and innovation as fundamental drivers of Africa’s socio-economic development and growth. It emphasizes the need to strengthen three main pillars: 1) Building/upgrading Research Infrastructures (RI), 2) Enhancing professional and technical competencies, and 3) Promoting entrepreneurship and innovation. It also highlights the importance of fostering collaboration among African countries and establishing partnerships with international stakeholders. ICT (Information and Communication Technologies) is identified as a critical priority sector. Areas such as computer science, telecommunications, cloud computing, big data, artificial intelligence, machine learning, security, and IoT offer potential for creating new knowledge, research capacities, and industries that address African and global challenges. DIGITAfrica aims to lay the foundations of a pan-African comprehensive RI in Digital Sciences, which will have a transformative impact on AU-EU shared R&I, as well as innovative education and training. DIGITAfrica will prepare the transformation of this partnership into a sustained research initiative in common strategic fields. Drawing from the expertise of partners across five AU countries, each with a strong background and representing diverse African contexts, alongside EU partners who coordinate the first two ESFRI DIGIT RIs dedicated to Digital Sciences, DIGITAfrica aims to exchange experiences, consult stakeholders, and foster dialogue to collaboratively develop a strategic approach. DIGITAfrica will serve as a catalyst for realizing the vision of a pan-African Digital RI and should become a cornerstone of Euro-African cooperation in R&I. The impact of DIGITAfrica will contribute to digitally transforming the African continent for prosperity and inclusivity, and to move with Europe towards a shared twin green and digital transition agenda.
Project Leader : Serge Fdida
01/01/2025
Repousser les Limites Usuelles Concernant l'Avenir des Réseaux Ubiquitaires
Project Leader : Sebastien Tixeuil
01/10/2024
End-to-end Cybersecurity to NEMO meta-OS
Horizon project 101070118 ΝΕΜΟ (Next Generation Meta OS) builds an IoT-Edge-Cloud continuum, in the form of an open-source, flexible, adaptable, and multi-technology meta-Operating System. NEMO aims to unleash the power of Artificial Intelligence IoT to increase European autonomy in data processing and lower CO2 footprint. Leveraging on consortium partners technological excellence, along with clear business and exploitation strategies, CyberNEMO builds on top of NEMO to add end-to-end cybersecurity and trust on IoT-Edge-Cloud-Data Computing Continuum. CyberNEMO will establish itself as a paradigm-shift to support resilience, risk preparedness, awareness, detection and mitigation within Critical Infrastructures deployments and across supply chains. To achieve technology maturity and massive adoption, CyberNEMO will not “reinvent the wheel”, but leverage on existing by-design, by-innovation, and by-collaboration zero-trust cybersecurity and privacy protection systems, and introduce novel concepts, methods, tools, testing facilities and engagement campaigns to go beyond today’s state of the art and create sustainable innovation, already evident within the project lifetime. CyberNEMO will offer end-to-end and full stack protection, ranging from a low level Zero-Trust Network Access layer up to a human AI explainable Situation Perception, Comprehension & Protection (SPCP) framework and tools, collaborative micro-cervices Auditing, Certification & Accreditation and a pan-European Knowledge Sharing, risk Assessment, threat Analysis and incidents Mitigation (SAAM) collaborative platform. Validation and penetration testing will take place in 6 pilots including OneLab for integration, various Critical Infrastructures (Energy, Water, Healthcare), media distribution, agrifood and fintech supply chain, along with their cross- domain, cross-border federation. Sustainability and adoption will be offered via the de-facto European Open source Eclipse Foundation ecosystem.
Project Leader :
02/09/2024
Greener Future Digital Research Infrastructures
GreenDIGIT tackles the challenge of reducing environmental impact of digital research infrastructures (RIs), which account for a growing share of global greenhouse gas emissions due to their high energy consumption. As research becomes increasingly data-intensive, ensuring that digital infrastructures operate sustainably is essential to align with the European Green Deal and UN SDGs. GreenDIGIT responds to this challenge by developing an integrated framework that will enhance the sustainability of digital RIs throughout their entire lifecycle. GreenDIGIT focuses on three major areas: technology innovation, strategic policy development, and capacity building. It established a reference architecture for sustainable RIs, and developing new tools for monitoring, assessing, and optimizing energy efficiency, and minimizing carbon footprints in data centres, cloud infrastructures, and networking components. These solutions will be deployed across four major European digital RIs—EGI, SLICES, SoBigData, and EBRAINS—modelling the entire ESFRI research landscape. GreenDIGIT also prioritizes scientific workflow optimization, integrating Reproducibility as a Service (RaaS) to help researchers design energy-conscious digital applications while ensuring data and experiment reproducibility. Additionally, it will provide policy recommendations, and potential binding-pathway mechanisms to guide RIs toward sustainable operations, along with training programs and a certification framework to equip RIs with the skills for energy-efficient digital services management. Through this approach, GreenDIGIT will not only improve the environmental footprint of participating RIs but also establish scalable best practices for digital RIs across Europe.
Project Leader : Serge Fdida
01/03/2024
SUstainable federation of Research Infrastructures for Scaling-up Experimentation in 6G
6G is expected to emerge as key enabler for the intelligent digital society of 2030 and beyond, providing superior performance via groundbreaking access technologies, such as joint communication and sensing, cell-free, Radio Intelligent Surfaces, and ubiquitous wireless intelligence . Most importantly, 6G is expected to trigger a total rethink of network architecture design, which builds on the key idea of new stakeholders entering into the value chain of future networks. The SUNRISE-6G approach is inspired by the “network of networks” concept of 6G Networks, aiming to integrate all private and public infrastructures under a massively scalable internet-like architecture. SUNRISE-6G similarly aspires to create a federation of 6G test infrastructures in a pan-european facility that will support converged Testing as a Service (TaaS) workflows and tools, a unified catalogue of 6G enablers publicly accessible by experimenters, and cross-domain vertical application onboarding. Experimentation and vertical application onboarding are offered via a Tenant Web Portal, that acts as a single-entry point to the facility, serving end users (e.g., experimenters) and tenants (e.g., vertical developers, infrastructure owners, 6G component manufactures). The project execution is based on 4 pillars, delivering: (a) the Implementation of new 6G enablers, complementary to existing ones being developed in SNS Phase 1 projects, (b) A truly scalable and 3GPP compliant Federation solution that provides access to heterogeneous resources and devices from all Europe, (c) A Federated AI plane aligned with AIaaS and MLOPS paradigms, which promotes a collaborative approach to AI research which benefits immensely from scaling-up datasets and models and (d) a commonly adopted Experimentation Plane, which offers common workflows to experimenters.
Project Leader : Serge Fdida
01/01/2024
6G Trans-Continental Edge Learning
Artificial Intelligence (AI) is widely studied and finding increasing adoption across communication technologies spanning network layers and business ecosystems. It is anticipated to play a central role in the design and operation of future 6G networks. Despite the promise of AI, there remain many obstacles to its use in communication networks. The introduction of software defined elements such as radio access network (RAN) intelligent controllers (RIC) enables multi-party applications for the control and management of networks. However, AI functions are still nascent and such structures do not extend to optical networks or multi-controller environments. 6G-XCEL seeks to address these challenges through research on high edge network use cases that employ multi-party AI controls running over compute accelerators to coordinate control across radio and optical networks. It will develop a reference framework for AI in 6G that will pave the way towards global validation, adoption and standardisation of AI approaches. This framework will enable decentralised AI-based network controls across network domains and physical layers, while promoting security and sustainable implementations. Using the latest AI algorithms and data compression, research on the resulting decentralised multi-party, multi-network AI (DMMAI) framework will enable the development of reference use cases, data and model repositories, curated training and evaluation data, as well as technologies for its use as a benchmarking platform for future AI/ML solutions for 6G networks. 6G-XCEL will bring together a large ecosystem of researchers from the EU and US to implement elements of the DMMAI framework in their testbeds and labs, integrating it into their research programs and validating the framework across platforms. Working with standardisation groups within each jurisdiction, 6G-XCEL will achieve joint progress towards large scale application of AI in 6G networks.
Project Leader : Serge Fdida
01/01/2024
Telecommunications and Computer Vision Convergence Tools for Research Infrastructures
Telecommunications and computer vision have evolved as separate scientific areas. This is envisioned to change with the advent of wireless communications with radios characterised by line-of-sight ranges which could benefit from visual data to predict the wireless channel dynamics. Computer vision applications will also become more robust if helped by radio-based imaging. This new joint research field relies on wireless communications, computer vision, sensing and machine learning, and it has a high innovation potential because of the large domain of innovative applications it enables and the relevant know-how available in Europe. However, the full potential of this new area can only be evaluated if adequate Research Infrastructures (RI) and tools are available. The main objective of the CONVERGE project is the development of an innovative toolset aligned with the motto “view-to-communicate and communicate-to-view”. This toolset is a world-first and consists of vision-aided large intelligent surfaces, vision-aided fixed and mobile base stations, a vision-radio simulator and 3D environment modeler, and machine learning algorithms for multimodal data including radio signals, video streams, RF sensing, and traffic traces. This toolset will be deployed into 7 RIs mostly aligned with the ESFRI SLICES-RI and improve their competitiveness. CONVERGE will also provide the scientific community with open datasets of experimental and simulated data obtained with the toolset in the RIs, meet scientific and industrial requirements by addressing relevant 6G verticals, enhance the competitiveness of the involved companies, extend the European influence to world-wide recognised RIs, enable the creation of new RIs, contribute to the development of new environment-friendly tools, and help European Union to address its societal challenges.
Project Leader : Serge Fdida
01/02/2023
Sécurité cognitive et programmable pour la résilience des réseaux de nouvelle génération
Sécurité cognitive et programmable pour la résilience des réseaux de nouvelle génération
Project Leader : Sebastien Tixeuil
01/10/2020
F-Interop - Services de tests d'interopérabilité à distance pour les objets connectés (IoT)
Services de tests d'interopérabilité à distance pour les objets connectés (IoT)
Project Leader : Serge FDIDA
01/01/2016
Équipe : PEQUAN
Floating-Point Transformer 4
Ce projet a pour objectif d’utiliser les grands modèles de langage pour aider à l’analyse et la transformation automatique de code flottant.
Project Leader : fabienne jezequel
01/10/2024
Algorithmes en précision mixte pour le calcul haute performance
Project Leader : Theo Mary
01/10/2023
Methods and Algorithms for Exascale
Project Leader : Pierre Jolivet
01/10/2023
https://numpex.org/exama-methods-and-algorithms-for-exascale/
Architectures Novatrices pour Capteur Fibre Optique Acoustique Distribué
Project Leader : Fabienne Jezequel
01/10/2023
HPDDM - high-performance unified framework for domain decomposition methods
HPDDM est une collection de préconditionneurs basés sur le paradigme de la décomposition de domaine, avec ou sans recouvrement. Ils peuvent être utilisés pour résoudre de grands systèmes linéaires, comme on en rencontre généralement lors de la discrétisation d'équations aux dérivées partielles. Ces préconditionneurs peuvent être utilisés avec diverses méthodes Krylov. La bibliothèque est utilisable dans les codes C, C++, Python ou Fortran.
Project Leader : Pierre JOLIVET
01/12/2022
Un jumeau numérique mécanique assisté par les splines et basé sur les images pour l'analyse de structures lattices réelles
Project Leader : Pierre Jolivet
01/10/2022
FiXiF - Reliable fixed-point implementation of linear signal processing (and control) algorithms
FiXiF est une suite d’outils utilisés pour implémenter des filters sur des systèmes embarqués (tels que DSP, micro-controlleurs, FPGA ou ASIC) avec l’impact de la précision finie (virgule fixe et flottante).
Project Leader : Thibault HILAIRE
01/08/2017
PROMISE - PRecision OptiMISE
PROMISE est un logiciel permettant de déterminer automatiquement la précision adéquate des variables dans un code numérique.
Project Leader : Fabienne JEZEQUEL
01/01/2016
ExBLAS - Exact Basic Linear Algebra Subprograms
ExBLAS fournit une version performante des algorithmes fondamentaux d'algèbre linéaire dont les résultats sont précis et reproductibles.
Project Leader : Stef GRAILLAT
01/01/2014
SAM - Stochastic Arithmetic in Multiprecision
SAM est une bibliothèque qui permet d'estimer et de contrôler la propagation des erreurs d'arrondi dans les programmes en précision arbitraire.
Project Leader : Fabienne JEZEQUEL
01/01/2010
CADNA - Control of Accuracy and Debugging for Numerical Application
CADNA est une bibliothèque qui permet de faire du calcul scientifique sur ordinateur en estimant et contrôlant la propagation des erreurs d'arrondi
Project Leader : Fabienne JEZEQUEL
10/01/1992
Équipe : PolSys
Post-Quantum Multivariate Cryptography
Le projet PQMC – Cryptographie Post-Quantique Multivariée vise à étudier, concevoir et implémenter de nouveaux schémas cryptographiques fondés sur des problèmes multivariés, dans le cadre de la transition vers des standards résistants à l’informatique quantique. Porté par le CNRS (coordinateur), le projet réunit sept partenaires académiques et industriels, dont le LIP6 (Sorbonne Université), qui se concentre sur les aspects algorithmiques, la sécurité asymptotique et les estimations de complexité des attaques. L’objectif est d’identifier des primitives cryptographiques robustes, efficaces et standardisables, notamment dans le contexte du processus de normalisation post-quantique engagé par le NIST. Le financement ANR alloué à Sorbonne Université pour le LIP6 s’élève à 217 494,38 €, couvrant principalement du personnel non permanent, du matériel scientifique et du fonctionnement. Durée du projet : 48 mois, à compter du 1er octobre 2025.
Project Leader : Mohab Safey
01/10/2025
Calcul Rapide de Relations Algébriques Multivariées
Project Leader : Vincent Neiger
01/10/2023
Algorithmes Efficaces pour Guessing, Inégalités, Sommation
Project Leader : Jeremy Berthomieu
01/10/2022
Équipe : QI
Quantum Internet Alliance - Phase 2
The long-term mission of the European QIA FPA is to Build a global Quantum Internet made in Europe. With this SGA2, QIA takes this ambition forward by: (1) Developing a full-stack prototype network validating all key sub-systems. The moonshot objective of QIA’s prototype network is to build two metropolitan scale networks containing quantum processors, connected by a long-distance fiber backbone using quantum repeaters, in the lab. This network will be fully programmable to allow the realization of any application supported by the hardware using platform-independent software. QIA’s prototype network serves as a unifying well-defined system-level integration target that aligns architectural, interface, and performance requirements across the hardware and software stack through its Systems Engineering (SE) Track—enabling coordinated development across a diverse, multidisciplinary technical consortium. In parallel, the Design Alternatives (DA) Track includes alternative hardware platforms and other technical solution ideas that address efficiency, scalability, and interoperability, and that have a clear potential to benefit QIA and beyond. (2) Preparing real-world deployments including proof-of-concept use case demonstrations as part of the Quantum Internet Initiative in the Quantum Europe Strategy. In this SGA, this includes the advancement of key components to higher TRL, and a market study of open access modalities, in preparation of pilot and open access facilities. QIA will also develop real-world use cases for the quantum internet, collaborating with end users to find solutions based on quantum internet functionalities, tailored to QIA platforms. (3) Driving an innovative European Quantum Internet ecosystem capable of scaling all sub-systems to world-leading European technology, including the open QIA Technology Forum.
Project Leader :
01/01/2026
Quantum Competitiveness Alignment, Scaling, and Support
QOMPASS aims at supporting the Quantum Flagship and providing the strategic backbone for implementing the Quantum Europe Strategy and preparing the governance and ecosystem required for the Quantum Act (2026). The project addresses the Work Programme’s objectives of strengthening Europe’s technological sovereignty, accelerating industrial uptake, and ensuring global leadership in quantum technologies. QOMPASS will achieve this through four integrated objectives: Strategic Intelligence & Roadmapping – Establish a European Quantum Observatory to deliver data-driven intelligence on investments, workforce, and supply chains, benchmark Europe’s position, and update the Strategic Research and Innovation Agenda (SRIA) with roadmaps aligned to the Quantum Europe Strategy. Visibility & Global Positioning – Build a strong European quantum brand, enhance outreach through high-impact communication, and position Europe as a trusted leader via flagship events (e.g., EQTC) and tier-1 media engagement. Ecosystem & Industrial Uptake – Accelerate lab-to-market transition by linking the Quantum Flagship with EuroHPC, Chips JU, DEP, and EIC; foster investment through European Quantum Scale-up Summits; and lead standardisation efforts to secure Europe’s influence on global norms. Governance & Policy – Support the EU’s new governance model under the Quantum Act, align national and EU strategies, enable joint funding initiatives, and prepare the governance blueprint for the next Multiannual Financial Framework (2028–2034). A Rapid Response Service ensures agility in addressing emerging EC requests. Building on the assets of previous CSAs (QFlag, QUCATS), QOMPASS unites Europe’s largest quantum industry association (QuIC), national agencies, and leading RTOs, creating a unique alliance to deliver at scale. QOMPASS will provide the strategic, operational, and governance support needed for Europe to achieve technological sovereignty and global leadership in quantum technologies.
Project Leader :
01/01/2026
Designing, Managing and Debugging Quantum Networks
QUESTING is a groundbreaking Doctoral Network (DN) initiative aimed at revolutionizing the field of Quantum Technology by addressing critical gaps in interdisciplinary education and training. This program will cultivate a new generation of "Q-System Innovators," equipping 15 doctoral candidates with expertise in quantum networks, hybrid classical-quantum systems, and interoperable cultural co-design. By integrating mathematics, physics, computing, and communications engineering with socio-cultural and ethical perspectives, QUESTING pioneers an innovative approach to building scalable, robust, and adaptive quantum systems. QUESTING tackles foundational challenges in quantum networking, including entanglement optimization, fault-tolerant design, and resource-efficient hybrid systems. By leveraging advanced methodologies such as small-world network modeling, Bayesian optimization, and quantum game theory, it addresses issues like the fragility of entanglement, network scalability, and adaptive fault detection. The program’s innovative approach encompasses the development of key performance indicators, physical models for noise and decoherence, and algorithms for resource management, ensuring seamless integration of quantum technologies with existing classical systems. The program's holistic methodology spans from theoretical advancements to real-world applications, including secure quantum communication, distributed resource management, and sustainable network topologies. Through its unique blend of participatory research, co-design processes, and industry-academic collaboration, QUESTING ensures alignment with global challenges such as cybersecurity, digital transformation, and equitable access to emerging technologies. This initiative is instrumental in advancing the European Union's Quantum Technologies Flagship and the UN Sustainable Development Goals, fostering innovation-driven growth while preparing Europe to lead responsibly in the quantum revolution.
Project Leader :
01/12/2025
Opérations quantiques d'ordre supérieur avec états connus
Project Leader : Marco Quintino
01/10/2025
Réseaux de capteurs quantique
Project Leader : damian markham
01/10/2024
Module de sécurité matériel pour le calcul dans un cloud quantique sécurisé
Project Leader : Elham Kashefi
01/01/2024
Quantum Secure Networks Partnership
The Quantum Secure Networks Partnership (QSNP) project aims at creating a sustainable European ecosystem in quantum cryptography and communication. A majority of its partners, which include world-leading academic groups, research and technology organizations (RTOs), quantum component and system spin-offs, cybersecurity providers, integrators, and telecommunication operators, were members of the European Quantum Flagship projects CIVIQ, UNIQORN and QRANGE. QSNP thus gathers the know-how and expertise from all technology development phases, ranging from innovative designs to development of prototypes for field trials. QSNP is structured around three main Science and Technology (ST) pillars. The first two pillars, “Next Generation Protocols” and “Integration”, focus on frontier research and innovation, led mostly by academic partners and RTOs. The third ST pillar “Use cases and Applications” aims at expanding the industrial and economic impact of QSN technologies and is mostly driven by companies. In order to achieve the specific objectives within each pillar and ensure that know-how transfer and synergy between them are coherent and effective, QSNP has established ST activities corresponding to the three main layers of the technology value chain, “Components and Systems”, “Networks” and “Cryptography and Security”. This framework will allow achieving the ultimate objective of developing quantum communication technology for critical European infrastructures, such as EuroQCI, as well as for the private information and communication technology (ICT) sectors. QSNP will contribute to the European sovereignty in quantum technology for cybersecurity. Additionally, it will generate significant economic benefits to the whole society, including training new generations of scientists and engineers, as well as creating high-tech jobs in the rapidly growing quantum industry.
Project Leader : Eleni Diamanti
08/11/2023
Un réseau quantique de capteurs distribués
Project Leader : Eleni Diamanti
01/10/2023
Ordinateurs quantique à base de lumière en variables discrètes et continues
Project Leader : Frederic Grosshans
01/10/2023
Scalable Continuous Variable Cluster State Quantum Technologies
Continuous variable (CV) quantum technologies have in recent years made significant impact on the fields of quantum communication, sensing, and computing, as signified by the detection of gravitational waves and demonstration of quantum advantage via Gaussian boson sampling. Moreover, the recent generation and manipulation of CV cluster states, comprising thousands of entangled modes, have direct implications for future developments of scalable CV quantum computing and networking systems. In CLUSTEC, we will pursue an interdisciplinary approach to unfold the full potential of CV cluster state technology by making conceptual and technical breakthroughs along three different directions. First, we will develop two complementary optical platforms for scalable generation of massive CV cluster states of different entanglement topologies and generation of hardware efficient error-correcting codes. The two systems will be based on a well-established low-loss fiber platform and the emerging, highly promising integrated photonics platform of thin-film Lithium Niobate. Second, we will develop and test radically new measurement-induced CV quantum computational and networking protocols and algorithms with certified quantum advantage and real-life applications. Third, we will explore and develop, theoretically and experimentally, novel quantum error-correcting CV protocols and technologies that facilitate the realization of practical fault-tolerant quantum technologies for quantum computing, communication and sensing with true scalability potential. With these activities, CLUSTEC will create a new path towards scalable quantum technologies and accelerate the development of practical quantum technologies with potentially radical impact on European society and economy. The results will pave the way for industrial uptake and exploitation in the near and long term, and in turn support the development of European leadership and autonomy in emerging strategic technologies.
Project Leader : Damian Markham
01/11/2022
Near term quantum devices: complexity, verification and applications
C22/1651
Project Leader : Alex Bredariol-Grilo
01/10/2022
Quantum Safe Internet
QSI aims at training a world-class cohort of doctoral candidates (DCs) capable of taking the next essential steps in the highly demanding area of cybersecurity. We aim to build strong lasting links between strategically selected industry and academic partners, in different disciplines, via the development of novel technologies for practical applications in data security. In parallel, we will also combine, via a collaborative long-term interdisciplinary approach, expertise in all relevant communities to address key fundamental problems in secure communications in the quantum era, and the important applications therein. The planned training network will provide research and training opportunities to a new generation of DCs, who, in the long-run, shall address the Grand Challenge of providing “Quantum-Safe Internet”, i.e., a communication infrastructure that is secure against not only classical attacks but also those enabled by quantum technologies. Today’s Internet security heavily relies on computational complexity assumptions, and as such is seriously threatened by advancements in quantum computing technologies. Indeed, we have recently witnessed a wave of key developments in this direction by a number of IT giants, e.g., Google, IBM, Microsoft, and Intel. This particularly jeopardizes applications that require long-term security. The number of such applications is continuously growing as more and more of our private information is stored and communicated in a digital way, e.g., electronic health records, which are now required by European legislation to remain secure for a long time. This requires us to urgently develop and implement new solutions, as we plan to do in this Doctoral Network (DN).
Project Leader : Eleni Diamanti
01/10/2022
Quantum technologies: Education and training to fulfill the strategic skill needs of research and industry in France
Project Leader : Eleni Diamanti
01/09/2022
Quantum communication testbeds
Project Leader : Eleni Diamanti
01/07/2022
Distribution quantique de clés avec des boîtes noires
Project Leader : Damian Markham
01/07/2022
Initiative Nationale Hybride HPC Quantique – R&D et Support des communautés
Project Leader : Damian Markham
01/04/2022
From NISQ to LSQ: bosonic corrector codes and LDPC
Project Leader : Frederic Grosshans
01/01/2022
Etude de la Pile Quantique : Algorithmes, modèles de calcul et simulation pour l’informatique quantique
Project Leader : Damian Markham
01/01/2022
High Performance Computer – Quantum Simulator hybrid
The aim of HPCQS is to prepare European research, industry and society for the use and federal operation of quantum computers and simulators. These are future computing technologies that are promising to overcome the most difficult computational challenges. HPCQS is developing the programming platform for the quantum simulator, which is based on the European ATOS Quantum Learning Machine (QLM), and the deep, low-latency integration into modular HPC systems based on ParTec’s European modular supercomputing concept. A twin pilot system, developed as a prototype by the European company Pasqal, will be implemented and integrated at CEA/TGCC (France) and FZJ/JSC (Germany), both hosts of European Tier-0 HPC systems. The pre-exascale sites BSC (Spain) and CINECA (Italy) as well as ICECH (Ireland) will be connected to the TGCC and JSC via the European data infrastructure FENIX. It is planned to offer quantum HPC hybrid resources to the public via the access channels of PRACE. To achieve these goals, HPCQS brings together leading quantum and supercomputer experts from science and industry, thus creating an incubator for practical quantum HPC hybrid computing that is unique in the world. The HPC-QS technology will be developed in a co-design process together with selected exemplary use cases from chemistry, physics, optimization and machine learning suitable for quantum HPC hybrid calculations. HPCQS fits squarely to the challenges and scope of the call by acquiring a quantum device with two times 100+ neutral atoms. HPCQS develops the connection between the classical supercomputer and the quantum simulator by deep integration in the modular supercomputing architecture and will provide cloud access and middleware for programming and execution of applications on the quantum simulator through the QLM, as well as a Jupyter-Hub platform with safe access guarantee through the European UNICORE system to its ecosystem of quantum programming facilities and application libraries.
Project Leader : Elham Kashefi
01/12/2021
Initiative Nationale Hybride HPC Quantique - Acquisition
Project Leader : Elham Kashefi
24/11/2021
Équipe : RO
DynaBBO - Dynamic Selection and Configuration of Black-box Optimization Algorithms
DynaBBO (Dynamic Selection and Configuration of Black-box Optimization Algorithms) is an ERC Consolidator research project started in October 2024 and coordinated by Carola Doerr. It aims at improving black-box optimisation technics; technics which are heavily relied-on by the industry sector and based on the repetition of experiments or numerical simulations to evaluate potential solutions to a problem.
Project Leader : carola doerr
01/10/2024
Algorithmes avec prédictions
Project Leader : Spyros Angelopoulos
01/10/2023
Bridging Black-box Optimization and Machine Learning for Dynamic Algorithm Configuration
Project Leader : Carola Doerr
01/10/2023
THéorie et observation Empirique pour Mesurer l’Influence dans les Structures sociales
In the literature of cooperative games, the notion of power index has been widely used to evaluate the inuence" of individual players (e.g., voters, political parties, nations, etc.) involved in a collective decision process, i.e. their ability to force a decision in situations like an electoral system, parliament, a governing council, a management board, etc. In practical situations, however, the information concerning the strength of coalitions and their e ective possibilities of cooperation is not easily accessible due to heterogeneous and hardly quanti able factors about the performance of groups, their bargaining abilities, moral and ethical codes and other psychological" attributes (e.g., the power obtained by threatening not to cooperate with other players). So, any attempt to numerically represent the inuence of groups and individuals conicts with the complex and multi-attribute qualitative nature of the problem. Previous applications of cooperative games show that this type of qualitative information is central for the evaluation of the individual inuence in voting systems and in social networks, the degree of acceptability of arguments in a debate, or the importance of criteria in a multi-criteria decision-making process, etc.
Project Leader : Fanny Pascual
Partenaires : université Paris Dauphine CNRS Hauts de France
01/10/2020
Équipe : SMA
Trustworthy AI for the Written Press
Project Leader : Gauvain Bourgne
01/01/2025
Applications et implications de l'intelligence artificielle dans la science
Project Leader : Jean-Gabriel Ganascia
01/01/2024
PEPR Ensemble - Gérer à l’échelle les collectifs de production de connaissance
Project Leader : Nicolas Maudet
01/09/2023
https://www.pepr-ensemble.fr/congrats-collaboration-a-grande-echelle/
Une plateforme argumentative pour la démocratie
Project Leader : Nicolas Maudet
01/01/2023
Équipe : SYEL
Architectures adaptatives pour l’intelligence artificielle embarquée
Project Leader : Andrea Pinna
01/10/2023
Intelligence artificielle embarquée et Capsules Ingérables (LabCom BodyCAP)
Project Leader : Andrea Pinna
01/01/2022