International Research Projects (IRP)
An IRP is a collaborative research project set up by one or more CNRS laboratories and laboratories from one or two other countries.
These projects enable the consolidation of established collaborations through short- or medium-term scientific exchanges. Their aims are to organise work meetings or seminars, develop joint research activities including field research and finally to supervise students. Teams from France and other countries must have already proved they are able to collaborate together, for example through one or more joint publications. IRPs last for five years. CNRS Informatics currently has 12 IRPs which correspond to strategic international collaborations.
ADONIS in Lebanon
The Intelligent Systems Diagnostics and Control Approaches IRP (ADONIS), 2020-2025, focuses on intelligent systems diagnostics and control. It brings together researchers from four partner organizations: Compiègne University of Technology (UTC), Faculty of Engineering – Lebanese University (UL), CNRS France and CNRS Lebanon, with common interests and a willingness to collaborate in the areas of control, data analysis, control of uncertainties and this in several frameworks of studies, such as in particular biomedical systems and transport systems. Three UTC/CNRS research units are involved in this IRP: Heuristics laboratory and diagnosis of complex systems (Heudiasyc - CNRS/Université de technologie de Compiègne), Roberval laboratory - Mechanical, acoustic and materials research unit (Roberval - CNRS/Université de technologie de Compiègne) and the Biomechanics and Bioengineering Laboratory (BMBI - CNRS/Université de technologie de Compiègne).
After many years of collaboration between these institutions, and particularly between UTC and UL since 1997, this project aims to consolidate and sustain this collaboration, to broaden its scope to new research themes, and increase its attractiveness and visibility.
See also: "Sustain and amplify Franco-Lebanese scientific collaboration"
APIER in Greece
One of the major current challenges in child-robot interaction within an educational framework is to enable effective and beneficial interactive learning over time. The robot must adapt online to different children and their progress. In return, the child should advance in their learning through interaction with the robot. The aim of this IRP, led by the Institut des systèmes intelligents et de robotique (ISIR – CNRS/Sorbonne University), is to strengthen a partnership with the Polytechnic University of Athens. In recent years, ISIR researchers have pioneered the implementation of online learning capabilities in humanoid robots during interactions with typically developing children or those with autism spectrum disorders. The goal now is to demonstrate that this provides a significant long-term educational benefit compared to pre-programmed robots.
FAIRGAME au Maroc
GeoGen3DHuman in Italy
The Geometric Deep Learning and Generative Models for 3D Human IRP (GeoGen3DHuman) between Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL - CNRS/Université de Lille/ Centrale Lille) and the Media Integration and Communication Center (MICC) is a joint research and collaboration in the area of Computer Vision and Artificial Intelligence. The core of GeoGen3DHuman is on developing mathematically principled generative frameworks for deep learning on non-Euclidean domains such as graphs and 3D meshes. GeoGen3DHuman touches upon some of the most challenging problems in different fields such as computer vision and graphics, where generative models are very crucial. The research topic itself is very timely in terms of need and applicability of the systems targeted. This research also seeks to advance fundamental tools, that are not only of high relevance in terms of intellectual merit but also in broad impact.
Specifically, it develops techniques for geometric deep learning on 3D meshes, generative models in non-Euclidean domains and applications that use 3D models of the face and of the human body
Keywords: artificial intelligence, geometric deep learning, 3D/4D human.
JMSL in the USA
The Joint Montpellier Stanford Laboratory (JMSL) is a partnership between the Montpellier Laboratory of Computer Science, Robotics and Microelectronics (LIRMM - CNRS/Université de Montpellier) and Stanford University. Its objective for the coming years is based on collaborations based on the three main axes presented below:
• underwater robotics,
• medical robotics,
• semantic web.
Ultimately, this collaboration aims to expand to other topics such as human-robot interactions, biomedical applications, data science, etc.
JOROWILA aux États-Unis
MAKC in the USA
The Modern Approaches to Knowledge Compilation IRP (MAKC) is centered on knowledge compilation (KC) for problem solving. KC is a research area which aims to preprocess information to improve the time required to solve highly-demanding computational tasks (i.e., solving NP and Beyond NP problems). The main objective of MAKC is to conceive and evaluate KC tools of various kinds (mainly preprocessors, compilers and reasoners) and to apply them to solve problems from a large spectrum of areas, for instance product configuration, formal verification, probabilistic inference, machine learning, and databases.
Keywords: artificial intelligence, deep solving, knowledge compilation
MLNS2 in Cameroon
The IRP Machine Learning, Network, System and Security (MLNS2) is interested in cybersecurity, which is a crucial research topic both in Cameroon and in France. It's mainly interested in two problems: the proliferation of malware on smartphones and phone call fraud that several African countries suffer from.
Created in 2022, MLNS2 associates in France CNRS and several laboratories namely Laboratoire d'Informatique en Images et Systèmes d'Information (LIRIS - CNRS/INSA de Lyon/Université Claude Bernard Lyon 1), Laboratoire d’Informatique de Grenoble (LIG, CNRS/Université Grenoble Alpes), Institut de recherche en informatique et systèmes aléatoires (IRISA - CNRS/Université de Rennes 1), and in Cameroon the University of Yaoundé I and its computer science laboratories.
Keywords: security, operating system, machine learning, networks, privacy.
PhraseoPrag au Japon
SINFIN in Argentina

The research carried out within the framework of the IRP Systems, Verification, Fundamental Training, LogIque, Statistics (SINFIN) focuses on the use of formal methods in the implementation of theories and automatic tools for modelling, verification and development of complex software.
Created in 2019, SINFIN succeeds the LIA Infinis which started in 2011. It associates the CNRS, the Université Paris Diderot, the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and the University of Buenos Aires.
Keywords: fundamental computing, Logic, Languages, Verification and Systems
The Trójkąt in Poland
Warsaw, Paris, and Bordeaux are major research centers in automata theory, logic, and game theory. The InternIRP) Le Trójkąt aims to structure and develop these collaborations, strengthening the historical ties between France and Poland in these fields. Since the 1990s, figures like Damian Niwinski, Igor Walukiewicz, and André Arnold have significantly contributed to these exchanges. Recent successes include solving complex problems such as the complexity of reachability in Petri nets and parity games, thanks to the work of researchers from the University of Warsaw, the l'Institut de recherche en informatique fondamentale (IRIF - CNRS/University of Paris) in Paris, and the Laboratoire bordelais de recherche en informatique (LaBRI - CNRS/Bordeaux INP/University of Bordeaux) in Bordeaux. The IRP Le Trójkąt seeks to expand these collaborations beyond the Paris-Bordeaux-Warsaw triangle, involving other institutions in France and Poland. Through organizing scientific events and supporting research, the project fosters cooperation to tackle major challenges in fundamental computer science.
NEUROCON aux Pays-Bas
OPTIROB en Tchéquie
L'IRP Optimization, Control, Robotics and Vision (OPTIROB) résulte de la campagne de 2025 pour une durée de cinq ans (2026-2030). Il implique des unités en France, le Laboratoire d'analyse et d'architecture des systèmes (LAAS-CNRS), et en Tchéquie, l'Université Technique Tchèque de Prague (CVUT). L'IRP OPTIROB formalise une collaboration de près de 30 ans entre Toulouse et Prague. Il s'appuie sur la complémentarité entre l'expertise du LAAS-CNRS en optimisation mathématique (notamment la hiérarchie moment-SOS) et les compétences du CVUT dans les domaines de la vision par ordinateur, de la mécanique et de la production industrielle. Le projet vise à développer des algorithmes d'optimisation globaux et scalables pour résoudre des problèmes d'ingénierie complexes en robotique, par la résolution de la cinématique inverse pour robots à haut degré de liberté et contrôle prédictif (MPC) assisté par la vision, en mathématiques avec l'optimisation sous contraintes d'équations aux dérivées partielles (EDP) et mécanique des milieux continus et en recherche opérationnelle avec l'ordonnancement industriel sous incertitude et théorie des jeux multi-agents.
L'IRP soutient une dynamique de recherche intégrée :
- Mobilité : échanges de doctorants, post-doctorants et chercheurs seniors entre les deux sites.
- Formation : co-supervision de thèses en cotutelle et organisation de workshops annuels conjoints.
- Innovation : transfert de méthodes théoriques vers des applications concrètes (conception de structures, manipulation robotique adaptative).
Porteurs : Didier Henrion, directeur de recherche CNRS au LAAS-CNRS, et Zdeněk Hanzálek, professeur à la CVUT
Participants clés : M. Korda, C. Artigues, N. Mansard (France) ; Z. Hurák, M. Kružík, T. Pajdla, J. Šivic (Tchéquie)
SANTAI au Canada
L’intelligence artificielle (IA) en santé promet une médecine plus précise et personnalisée, mais son adoption clinique reste limitée. Les principaux freins sont le manque de généralisation, la faible robustesse et l’opacité des décisions. L’IRP Fiabilisation et transparence des modèles d’intelligence artificielle en santé (SANTAI) vise à développer des solutions innovantes en IA intégrant des connaissances spécifiques au domaine médical. L’objectif est de renforcer la généralisation et la transparence des mécanismes décisionnels, considérés comme des leviers clés pour assurer un transfert clinique fiable. Les méthodes développées seront évaluées dans le cadre du diagnostic des pathologies cardiaques et neurodégénératives, deux domaines à fort impact en santé publique.
Créé en 2026, SANTAI associe en France le Centre de recherche en acquisition et traitement de l'image pour la santé (CREATIS-CNRS/INSA Lyon/Inserm/Université Claude Bernard Lyon 1) et, au Canada, les laboratoires VITALab et SNAIL, rattachés à l’Université de Sherbrooke.
Porteur : Olivier Bernard, professeur à l'INSA de Lyon et membre du CREATIS