Projects
Team : ALMASTY
RĂ©sistance des systĂšmes cryptographiques
Le projet Cryptanalyse sâintĂ©resse Ă lâĂ©tude et Ă la standardisation des primitives cryptographiques. En effet la cryptographie moderne est devenue un outil indispensable pour sĂ©curiser les communications personnelles, commerciales et institutionnelles. Ce projet permettra de fournir, une estimation des difficultĂ©s de rĂ©soudre les problĂšmes sous-jacents et dâen dĂ©duire le niveau de sĂ©curitĂ© que confĂšre lâutilisation de ces primitives.
La problĂ©matique est lâĂ©valuation de la sĂ©curitĂ© des algorithmes cryptographique.
Project Leader : Charles Bouillaguet
10/01/2023
Tailles de clés : optimisations pratiques et théoriques et approches modernes pour des estimations précises du coût de NFS
Project Leader : Charles Bouillaguet
10/01/2021
Calcul réparti sécurisé : Cryptographie, Combinatoire, Calcul Formel
Project Leader : Damien Vergnaud
09/10/2021
Team : ALSOC
Vérification formelle et résilience aux attaques physiques de contre-mesures matérielles
Project Leader : Emmanuelle Encrenaz
04/08/2025
Modélisation et Vérification pour CPS Sécurisés et Performants
Project Leader : Daniela Genius
10/01/2023
Conception de systÚmes sécurisés par une réduction des effets de la micro-architecture sur les attaques par canaux auxiliaires
Project Leader : Quentin Meunier
10/01/2020
TSAR - TSAR (Tera-Scale ARchitecture)
Project Leader : Alain Greiner
09/27/2017
Team : APR
DĂ©veloppement une solution Open Source Intelligence (OSINT) pour surveiller, tracer et analyser les transactions de cryptomonnaies
Project Leader : Emmanuel Chailloux
01/01/2025
Analyse de paramĂštres de classes de DAGS
Project Leader : Antoine Genitrini
10/01/2023
Leveraging Software Heritage to Enhance Cybersecurity
Project Leader : Antoine Mine
10/01/2023
Raisonnements formellement certifiés en apprentissage automatique
Project Leader : Antoine Mine
10/01/2023
Improving Digital Systems Security Evaluation
Project Leader : Antoine Mine
07/01/2022
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
10/01/2020
Team : BD
Enrichissement d'une base de connaissances à partir de données prosopographiques médiévales incertaines
Project Leader : Camelia Constantin
11/01/2025
https://scanr.enseignementsup-recherche.gouv.fr/projects/ANR-25-CE38-0700
experimaestro - Computer science experiment scheduler and manager
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 is a web page segmentation algorithm based on an hybrid approach for scanned document segmentation and visual-based content segmentation. A web page is associated with three structures: the DOM tree, the content structure and the logical structure. The DOM tree represents the HTML elements of a page, the geometric structure organizes the content based on a category and its geometry and finally the logical structure is the result of mapping content structure on the basis of the human-perceptible meaning that conforms the blocks. The segmentation process is divided in three phases: analysis, understanding and reconstruction of a web page. An evaluation method is proposed in order to perform the evaluation of web page segmentations based on a ground truth of 400 pages classified into 16 categories. A set of metrics are presented based on geometric properties of blocks. Satisfactory results are reached when comparing to other algorithms following the same approach.
Project Leader : AndrĂšs SANOJA
01/01/2012
Team : 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 Mooreâs 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
12/01/2024
SystĂšmes Bio-inspirĂ©s distribuĂ©s de confiance : bases thĂ©oriques et mise en Ćuvre matĂ©rielle
Project Leader : Haralampos Stratigopoulos
10/01/2023
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 âdeadâ 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
10/01/2023
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
09/01/2023
ComprĂ©hension et attĂ©nuation dâerreur dans les implĂ©mentations analogiques de rĂ©seaux de neurones sur silicium
Project Leader : Haralampos Stratigopoulos
10/01/2022
Récupération d'énergie mécanique proche des limites physiques par synthÚse adiabatique de la dynamique électromécanique
C23/0800
Project Leader : Dimitri Galayko
10/01/2022
CORIOLIS - Platform for physical synthesis of integrated circuits
Coriolis is an experimental integrated platform for the research, development and evaluation of new back-end VLSI design flows. Interconnect scaling to nanometer processes presents many difficult challenges to CAD flows. Currently academic research on back-end tend to address only specific algorithmic issues separately, although one key issue to address is the cooperation of multiple algorithmic tools. CORIOLIS, our platform, is based on an integrated C++ database around which all tools consistently interact and collaborate. This platform currently includes a timing-driven global place and route flow.
Project Leader : Jean-Paul CHAPUT
01/01/2004
CAIRO - Analog IP Design
Our purpose is to provide a language for designing generators of analog functions, that can be easily ported to new set of specfications and new technologogy processes. We are currently developing such a language that is called CAIRO+
The CAIRO+ language supports the four steps of a design flow based on net-list and layout templates. This language is aimed to help the designer to capture his knowledge, thus creating a library of layout-aware analog functions. It is based on C++ language. The design flow relevant to CAIRO+ is the following :
->net-list and layout template capture, ->design space exploration (managing electrical constraints) ->shape function computation (managing geometrical constraints) ->layout generation (place and route) CAIRO+ allows creating complex hierarchical analog function generators by using existing generators of simpler functions. It is an answer to the problem of Analog and Mixed IPs.
As a demonstration of the CAIRO+'s capabilities, we are developping Analog to Digital converters, specially Sigma Delta.
Project Leader : Marie-Minerve LOUĂRAT
01/01/2004
Team : ComplexNetworks
Pathways : Trajectoires â NumĂ©rique sobre et rĂ©silient face aux limites planĂ©taires et aux risques de basculements
Le dĂ©veloppement du numĂ©rique, que ce soient ses dĂ©veloppements technologiques ou ses usages, a jusquâici reposĂ© sur une croissance forte. Les contraintes de ressources, les limites planĂ©taires et les enjeux environnementaux remettent aujourdâhui cette croissance en question. Ce projet est dĂ©diĂ© Ă lâexploration de ce que sera un monde oĂč le numĂ©rique stagnerait, voire serait dĂ©croissant, intermittent, et non-fiable. Il ne sâagit pas ici de concevoir un numĂ©rique plus robuste, câest-Ă -dire capable de faire face aux alĂ©as, mais un numĂ©rique et une sociĂ©tĂ© rĂ©silients, câest-Ă -dire capables de vivre et traverser des dĂ©faillances, mĂȘmes majeures, brutales et imprĂ©vues.
Porteurs: Matthieu Latapy (LIP6 - CNRS, Sorbonne Univ.), Philippe Ciblat (LTCI - Telecom Paris, IP Paris), Philippe Durance (CNAM)
Project Leader : Matthieu Latapy
01/01/2026
Team : DECISION
Algorithmes pour la prise de décision et l'apprentissage des préférences en optimisation multi-objectifs
Project Leader : Nawal Benabbou
10/01/2024
aGrUM - a Graphical Unified Model
aGrUM is a C++ library designed for easily manipulating graphical models. Its range of applications is quite large as it is designed, e.g., for performing learning tasks (for instance, learning Bayes nets from data), planning tasks (FMDPs) and inference (Bayes nets, GAI-nets, influence diagrams).
Project Leader : Christophe GONZALES & Pierre-Henri WUILLEMIN
Team : DELYS
Algorithmes distribués frugaux au coeur des réseaux
Project Leader : Pierre Sens
10/01/2024
Vers des applications serverless correctes par construction
Project Leader : pierre sens
10/01/2024
https://scanr.enseignementsup-recherche.gouv.fr/projects/ANR-24-CE25-5598
Désagrégation virtualisée
Project Leader : Julien Sopena
09/01/2023
Un nouveau paradigme de donnée : Les données autonomes et intelligentes
Project Leader : Franck Petit
10/01/2022
Team : LFI
Pelvic neRves autOmatic Segmentation using hybrId Trustworthy AI
Project Leader : Isabelle Bloch
10/01/2025
PostGenAI - PAC 1.6 - AI for industry and Risk Management
Project Leader : Marie-Jeanne Lesot
01/01/2025
PostGenAI - PAC 2.4 - AI, Images and Models for Medicine
Project Leader : Isabelle Bloch
01/01/2025
PostGenAI - PAC 3.6 - Maritime Risks AI-Geoint
Project Leader : Marie-Jeanne Lesot
01/01/2025
Exploitation de modĂšles d'explications pour les algorithmes d'apprentissage profond
Project Leader : Christophe Marsala
10/01/2024
Histoire des agences d'images et vision par ordinateur
Project Leader : Isabelle Bloch
10/01/2024
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
10/01/2022
Premature Human Connectome Patterns: mapping the fetal brain development using extreme field MRI
Project Leader : Isabelle Bloch
10/01/2021
Team : MOCAH
PostGenAI - PAC 3.3 - Integrated Multimodal AI to Adapt Learning Content to Multiple Users Needs
Le projet présenté poursuit deux objectifs principaux :
DĂ©velopper des modĂšles dâapprentissage adaptatifs rĂ©utilisables et flexibles, capables de sâadapter Ă diffĂ©rents profils dâapprenants et systĂšmes, en sâappuyant sur une approche post-IA gĂ©nĂ©rative.
Accompagner lâintĂ©gration de lâIA gĂ©nĂ©rative en Ă©ducation Ă travers la formation, le partage de mĂ©thodes et des recommandations, afin dâĂ©valuer son rĂŽle dans la transformation et lâindustrialisation de la formation.
LâIA gĂ©nĂ©rative permet Ă©galement de remplacer certaines tĂąches peu crĂ©atives, libĂ©rant du temps pour les enseignants et favorisant leur crĂ©ativitĂ©.
La recherche repose sur une dĂ©marche participative pour concevoir les systĂšmes adaptatifs, et sur lâobservation des processus industriels pour analyser lâimpact de lâIA gĂ©nĂ©rative. Enfin, plusieurs initiatives existent dĂ©jĂ dans ce domaine, notamment au laboratoire LIP6, qui a contribuĂ© au partage de donnĂ©es et de modĂšles Ă©ducatifs.
Project Leader : Vanda Luengo
01/01/2025
IA pour la personnalisation de rétroactions dans l'apprentissage de la pensée informatique par le jeu
Les environnements d'apprentissage par le jeu peuvent favoriser l'apprentissage de la pensĂ©e informatique et de la programmation dans l'enseignement primaire et secondaire, en permettant aux apprenants de concevoir et exĂ©cuter des algorithmes au moyen de blocs de programmation pour avancer dans le jeu. Cependant, ces environnements peuvent poser des difficultĂ©s aux Ă©tudiants en raison de leur caractĂšre peu contraint demandant de l'autonomie et de l'autorĂ©gulation. Des Ă©tudes ont montrĂ© que cette difficultĂ© peut ĂȘtre en partie rĂ©duite par l'entremise de rĂ©troactions pouvant aider l'apprenant Ă avancer dans la crĂ©ation de leurs algorithmes. Dans ce projet, nous souhaitons crĂ©er et Ă©valuer dans ces environnements des rĂ©troactions qui sont guidĂ©es par une IA, qui aurait pour tĂąche de prĂ©dire le niveau d'aide qu'un apprenant a besoin, via leur solution courante, leur comportement dans l'environnement et leur attention visuelle. Cette recherche se fera en deux Ă©tapes. D'abord, nous utiliserons des traces multimodales de l'apprenant pour prĂ©dire en temps rĂ©el le besoin d'aide des apprenants lors de la conception d'algorithmes. Ces prĂ©dictions feront office de politique d'aide personnalisĂ©e pour dĂ©clencher l'Ă©mission d'une rĂ©troaction. DeuxiĂšmement, nous examinerons comment formuler et dĂ©livrer ces rĂ©troactions, en considĂ©rant notamment les agents pĂ©dagogiques qui peuvent interagir de façon plus naturelle avec l'apprenant. Nous utiliserons deux jeux sĂ©rieux dĂ©diĂ©s Ă l'apprentissage de la pensĂ©e informatique et de la programmation pour ce faire, Pyrates et Spy. L'objectif de ce projet est de concevoir un modĂšle guidĂ©e par l'IA pour la personnalisation de rĂ©troactions sur des connaissances liĂ©es Ă la pensĂ©e informatique, ainsi que des rĂ©sultats sur la meilleures maniĂšres de fournir ces rĂ©troactions. Ă long terme, nous visons Ă faciliter l'adoption Ă l'Ă©cole de ces jeux personnalisĂ©s.
Project Leader : Sebastien Lalle
10/01/2024
Simulateur haptique pour l'apprentissage de la spasticité
La spasticitĂ© est un trouble moteur caractĂ©risĂ© par une hyperactivitĂ© musculaire provoquĂ© par lâalĂ©tration de la conduction nerveuse. Le diagnostic de cette pathologie repose sur lâĂ©valuation du degrĂ© de rĂ©sistance du membre suite Ă un mouvement passif rĂ©alisĂ© par le praticien et sert Ă dĂ©terminer le traitement Ă suivre. Cependant cette Ă©valuation reste subjective et requiert de lâexpĂ©rience de la pratique. Seul un entraĂźnement sur patient rĂ©el permet de gagner de lâexpĂ©rience. Câest dans ce contexte que le projet HASPA a pour but de dĂ©velopper un simulateur permettant de reproduire diffĂ©rents degrĂ©s de spasticitĂ© pour permettre aux jeunes praticiens de sâexercer avant de pratiquer leurs gestes sur patient.
Project Leader : Vanda Luengo
Partners : 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.
10/01/2022
AdaptivâMath - AdaptivâMath
obtenu dans le cadre du Partenariat d'Innovation Intelligence Artificielle (P2IA) du ministÚre 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'élÚves (textit{clustering}) appris sur l'ensemble des classes sur la base de critÚres de maßtrise 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'élÚves, afin de faciliter la mise en place de stratégies de pédagogie différenciée.
Project Leader : François Bouchet
10/01/2019
MAGAM - Multi-Aspect Generic Adaptation Model
MAGAM is a Multi-Aspect (didactic, pedagogic, affective and motivational, gaming, etc.) Generic Adaptation Model based on matrix calculation that aims to adapt learning activities.
Project Leader : Vanda LUENGO et Baptiste MONTERRAT
03/01/2016
LEA4PA - LEarning Analytics for Adaptation and Personnalisation
This project aims to built a plateform to assist teachers in adapting learning activities. Multiple indicators (cognitive, pedagogical, temporal, etc. will be inferred from data traces that are recorded and generated automatically or manually from the learner activities. Visualization systems will be proposed to assist teachers in their activities' adaptation process and make it.
Project Leader : Vanda LUENGO et Amel YESSAD
01/01/2016
Team : MoVe
Eco Design : Approches centrĂ©es sur lâhumain pour la conception dâobjets numĂ©riques plus soutenables
Project Leader : adel noureddine
01/01/2026
Réduire l'empreinte énergétique des logiciels grùce aux changements de comportement des utilisateurs
Project Leader : Adel Noureddine
09/01/2025
BCMCyPhy - Component model for cyber-physical control systems
This research project and its associated software aim at designing and implementing a software component model for cyber-physical control systems. It develops over the BCM4Java project from which it uses the basic concepts and the implementation of distributed components in Java (10.000 lines of code and documentation today). Besides integrating real time components, this project studies component-based software architectures for control, their specification using stochastic hybrid systems and their simulation using models following the DEVS standard. A particular focus is given on the parallel composability between components, their individual specifications and simulation models. A software subproject of BCMCyPhy proposes a new implementation in Java of the DEVS standard for modular simulation of components and their assemblies (20.000 lines of code and documentation today). The simulators obtained through the composition of the components simulator models allow to debug, test, verify and validate applications. This software has been used by a few tens of students and is still being used in the context of the ALASCA master 2 course since 2018.
Project Leader : Jacques MALENFANT
06/18/2019
PNML Framework
PNML Framework is a prototype implementation of ISO/IEC-15909 part 2, International Standard on Petri Net Markup Language. The primary purpose of PNML is to enable interoperability among Petri net tools. PNML framework has thus been designed to back the Standard. It will enable Petri nets tools developers to seamlessly integrate PNML support into their tools. It provides an extensive and comprehensible API to create, save, load and browse PNML models.
Project Leader : Fabrice KORDON
04/01/2005
CPN-AMI
is a Petri Net based CASE environment. It offers a set of services to perform specification, validation, formal verification, model checking, compute structural properties (invariants, traps, syphons etc.) simulate and generate code. These services have been implemented either by members of our team or university partners (Technical university of Helsinki, University of Torino, Technical university of Munchen, Bell laboratories). The second geration of CPN-AMI, build on top of FrameKit, is available on the Internet since March 1997.
Project Leader : Fabrice KORDON
12/01/1994
SPOT - Spot Produces Our Traces
SPOT (Spot Produces Our Traces) est une bibliothÚque 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 bibliothÚque 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
Team : NPA
Scientific Large-scale Infrastructure for Computing/ Communication Experimental Studies â Implementation Phase
Project Leader : Serge Fdida
06/01/2026
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 Africaâs 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 RĂ©seaux Ubiquitaires
Project Leader : Sebastien Tixeuil
10/01/2024
End-to-end Cybersecurity to NEMO meta-OS
Horizon project 101070118 ÎÎÎÎ (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 âreinvent the wheelâ, 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 todayâs 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 :
09/02/2024
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 RIsâEGI, SLICES, SoBigData, and EBRAINSâmodelling 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
03/01/2024
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 ânetwork of networksâ 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
Tester et évaluer une architecture pour la 6G composée de noeuds métamorphiques pour aboutir à la réalisation d'un prototype
Project Leader : Anne Fladenmuller
06/13/2023
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
10/01/2020
F-Interop - Remote interoperability testing services for IoT devices
Remote interoperability testing services for IoT devices
Project Leader : Serge FDIDA
01/01/2016
Team : PEQUAN
Floating-Point Transformer 4
Ce projet a pour objectif dâutiliser les grands modĂšles de langage pour aider Ă lâanalyse et la transformation automatique de code flottant.
Project Leader : fabienne jezequel
10/01/2024
Algorithmes en précision mixte pour le calcul haute performance
Project Leader : Theo Mary
10/01/2023
Methods and Algorithms for Exascale
Project Leader : Pierre Jolivet
10/01/2023
https://numpex.org/exama-methods-and-algorithms-for-exascale/
Architectures Novatrices pour Capteur Fibre Optique Acoustique Distribué
Project Leader : Fabienne Jezequel
10/01/2023
HPDDM - high-performance unified framework for domain decomposition methods
HPDDM is a collection of preconditioners based on domain decomposition, either overlapping or non-overlapping. They can be used to solve large linear systems, as typically encountered when discretizing partial differential equations. These preconditioners can be used in conjunction with various Krylov methods. The library is usable in C, C++, Python, or Fortran codes.
Project Leader : Pierre JOLIVET
12/01/2022
Un jumeau numérique mécanique assisté par les splines et basé sur les images pour l'analyse de structures lattices réelles
Project Leader : Pierre Jolivet
10/01/2022
FiXiF - Reliable fixed-point implementation of linear signal processing (and control) algorithms
FiXiF is a suite of tools used to implement filters on embedded devices (usually DSP, micro-controllers, FPGA or ASIC) with finite-precision impact in minds (fixed- or floating-point arithmetic).
Project Leader : Thibault HILAIRE
08/01/2017
PROMISE - PRecision OptiMISE
PROMISE is a tool to auto-tune the precision of floating-point variables in numerical codes.
Project Leader : Fabienne JEZEQUEL
01/01/2016
ExBLAS - Exact Basic Linear Algebra Subprograms
ExBLAS aims at providing algorithms and implementations for fundamental linear algebra operations (like those included in the BLAS library) that deliver reproducible and accurate results with small or without losses to their performance on modern parallel architectures.
Project Leader : Stef GRAILLAT
01/01/2014
SAM - Stochastic Arithmetic in Multiprecision
The SAM library enables rounding error estimation in arbitrary precision programs.
Project Leader : Fabienne JEZEQUEL
01/01/2010
CADNA - Control of Accuracy and Debugging for Numerical Application
CADNA is a library which allows to perform scientific computations with the estimation and the control of the round-off error propagation.
Project Leader : Fabienne JEZEQUEL
01/10/1992
Team : PolSys
Post-Quantum Multivariate Cryptography
Le projet PQMC â Cryptographie Post-Quantique MultivariĂ©e vise Ă Ă©tudier, concevoir et implĂ©menter de nouveaux schĂ©mas cryptographiques fondĂ©s sur des problĂšmes multivariĂ©s, dans le cadre de la transition vers des standards rĂ©sistants Ă lâinformatique quantique. PortĂ© par le CNRS (coordinateur), le projet rĂ©unit sept partenaires acadĂ©miques et industriels, dont le LIP6 (Sorbonne UniversitĂ©), qui se concentre sur les aspects algorithmiques, la sĂ©curitĂ© asymptotique et les estimations de complexitĂ© des attaques. Lâobjectif est dâidentifier des primitives cryptographiques robustes, efficaces et standardisables, notamment dans le contexte du processus de normalisation post-quantique engagĂ© par le NIST. Le financement ANR allouĂ© Ă Sorbonne UniversitĂ© pour le LIP6 sâĂ©lĂšve Ă 217âŻ494,38 âŹ, couvrant principalement du personnel non permanent, du matĂ©riel scientifique et du fonctionnement. DurĂ©e du projet : 48 mois, Ă compter du 1er octobre 2025.
Project Leader : Mohab Safey
10/01/2025
Calcul Rapide de Relations Algébriques Multivariées
Project Leader : Vincent Neiger
10/01/2023
Algorithmes Efficaces pour Guessing, Inégalités, Sommation
Project Leader : Jeremy Berthomieu
10/01/2022
Team : 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 QIAâs 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. QIAâs 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) Trackâenabling 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 Programmeâs objectives of strengthening Europeâs technological sovereignty, accelerating industrial uptake, and ensuring global leadership in quantum technologies. QOMPASS will achieve this through four integrated objectives: Strategic Intelligence & Roadmapping â Establish a European Quantum Observatory to deliver data-driven intelligence on investments, workforce, and supply chains, benchmark Europeâs position, and update the Strategic Research and Innovation Agenda (SRIA) with roadmaps aligned to the Quantum Europe Strategy. Visibility & Global Positioning â 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 â 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 Europeâs influence on global norms. Governance & Policy â Support the EUâs 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 (2028â2034). A Rapid Response Service ensures agility in addressing emerging EC requests. Building on the assets of previous CSAs (QFlag, QUCATS), QOMPASS unites Europeâs 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 programâs 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 :
12/01/2025
Opérations quantiques d'ordre supérieur avec états connus
Project Leader : Marco Quintino
10/01/2025
Memory Enhanced Transduction of Quantum processors
Project Leader : Eleni Diamanti
03/01/2025
RĂ©seaux de capteurs quantique
Project Leader : damian markham
10/01/2024
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, âNext Generation Protocolsâ and âIntegrationâ, focus on frontier research and innovation, led mostly by academic partners and RTOs. The third ST pillar âUse cases and Applicationsâ 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, âComponents and Systemsâ, âNetworksâ and âCryptography and Securityâ. 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
11/08/2023
Un réseau quantique de capteurs distribués
Project Leader : Eleni Diamanti
10/01/2023
Ordinateurs quantique Ă base de lumiĂšre en variables discrĂštes et continues
Project Leader : Frederic Grosshans
10/01/2023
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
11/01/2022
Near term quantum devices: complexity, verification and applications
C22/1651
Project Leader : Alex Bredariol-Grilo
10/01/2022
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 âQuantum-Safe Internetâ, i.e., a communication infrastructure that is secure against not only classical attacks but also those enabled by quantum technologies. Todayâs 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
10/01/2022
Quantum technologies: Education and training to fulfill the strategic skill needs of research and industry in France
Project Leader : Eleni Diamanti
09/01/2022
Quantum communication testbeds
Project Leader : Eleni Diamanti
07/01/2022
Distribution quantique de clés avec des boßtes noires
Project Leader : Damian Markham
07/01/2022
Initiative Nationale Hybride HPC Quantique â R&D et Support des communautĂ©s
Project Leader : Damian Markham
04/01/2022
From NISQ to LSQ: bosonic corrector codes and LDPC
Project Leader : Frederic Grosshans
01/01/2022
Etude de la Pile Quantique : Algorithmes, modĂšles de calcul et simulation pour lâinformatique quantique
Project Leader : Damian Markham
01/01/2022
High Performance Computer â 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 ParTecâs 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
12/01/2021
Initiative Nationale Hybride HPC Quantique - Acquisition
Project Leader : Elham Kashefi
11/24/2021
Team : 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
10/01/2024
Algorithmes avec prédictions
Project Leader : Spyros Angelopoulos
10/01/2023
Bridging Black-box Optimization and Machine Learning for Dynamic Algorithm Configuration
Project Leader : Carola Doerr
10/01/2023
THĂ©orie et observation Empirique pour Mesurer lâInfluence 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
Partners : université Paris Dauphine CNRS Hauts de France
10/01/2020
Team : SMA
Applications et implications de l'intelligence artificielle dans la science
Project Leader : Jean-Gabriel Ganascia
04/01/2026
Ăvaluer et attĂ©nuer la vulnĂ©rabilitĂ© individuelle Ă lâinfluence en ligne
Project Leader : Jean-Gabriel Ganascia
04/01/2026
PostGenAI - PAC 2.3 - Digital âIâ Twin
Project Leader : Vincent Corruble
01/01/2025
PostGenAI - PAC 3.5 - AI for Augmented Deliberation
Project Leader : Nicolas Maudet
01/01/2025
Une IA de confiance pour l'analyse de la presse Ă©crite
Project Leader : Gauvain Bourgne
01/01/2025
THEMIS
Project Leader : Jean-Gabriel Ganascia
01/01/2024
GĂ©rer Ă lâĂ©chelle les collectifs de production de connaissance
Project Leader : Nicolas Maudet
09/01/2023
https://www.pepr-ensemble.fr/congrats-collaboration-a-grande-echelle/
Une plateforme argumentative pour la démocratie
Project Leader : Nicolas Maudet
01/01/2023
Team : SYEL
Compression de nuages de points 3D pour les humains et les machines
Project Leader : Hichem Sahbi
01/01/2026
Composants Matériels et Logiciels pour l'IA
Project Leader : Andrea Pinna
01/01/2026
Architectures adaptatives pour lâintelligence artificielle embarquĂ©e
Project Leader : Andrea Pinna
10/01/2023
Intelligence artificielle embarquée et Capsules Ingérables (LabCom BodyCAP)
Project Leader : Andrea Pinna
01/01/2022