List of CNRS Informatics tenure tracks 2026

The scientific objective is to be able to exploit massive, clinically contextualised imaging data, in order to design, develop, evaluate and validate new approaches to analysing this data, with a much more integrated, transdisciplinary and translational vision. This involves integrating heterogeneous data and modelling patient care trajectories, including medical images, radiological reports, hospital and biological data. This should enable these multimodal data to be monitored longitudinally, in order to identify the factors that determine the effectiveness of treatments. The aim is also to identify and validate complex biomedical signatures from multimodal and multiscale sources (multi-parametric MRI, EEG, ECG, ultrasound imaging, biomolecular, genetic and phenotypic data, etc.), in order to reveal underlying pathological mechanisms and guide therapeutic approaches.

Host laboratories :

• Centre de recherche en acquisition et traitement d'images pour la santé (CREATIS - CNRS/Inserm/INSA Lyon/Université Claude Bernard Lyon 1)
• Laboratoire des sciences de l’ingénieur, de l’informatique et de l’imagerie (ICube - CNRS/Université de Strasbourg)
• Laboratoire recherche translationnelle et innovation en médecine et complexité (TIMC - CNRS/Université Grenoble Alpes)

Institutions :

• INSA Lyon
• Université Grenoble Alpes
• Université de Strasbourg, Institut interdisciplinaire de Neurosciences de Strasbourg

There remains a strong and ongoing need in the development, design, and semantics of programming languages to provide the most contextually appropriate abstractions possible. This is particularly valuable for producing compilers and mechanized verification tools. For instance, one might consider programming languages like Rust, which offers safe yet optimized memory management, making it suitable for systems programming applications. Alternatively, domain-specific languages for parallelism can bridge the gap between deterministic specifications and target embedded code capable of running on parallel architectures (e.g., multi-core systems).
In most cases, these abstractions are achieved through the design of novel type systems, whose properties are formally proven. Exploiting these systems requires the development of modern compilation and verification techniques to generate code that is both safe and efficient. These research themes are actively explored by communities that publish in conferences such as POPL, PLDI, ICFP, OOPSLA, and ECOOP.

Host laboratories :

• CDépartement d'informatique de l'École Normale Supérieure (DIENS - CNRS/ENS PSL)
• Laboratoire de l'informatique du parallélisme (LIP - CNRS/ENS de Lyon/Lyon 1 Université)
• Laboratoire méthodes formelles (LMF - CNRS/ENS Paris-Saclay/Université Paris-Saclay)
• VERIMAG (CNRS/Université Grenoble Alpes)

Institutions :

• ENS Lyon
• ENS Paris Saclay
• ENS PSL
• Université Grenoble Alpes

Today, optimization methods now form a fundamental cornerstone for tackling complex problems in computer science, across various scientific fields (e.g., bioinformatics, neuroscience, climatology, astrophysics, health, etc.), as well as industrial contexts, societal organization, and beyond. The goal is to recruit a Junior Professor (CPJ) whose research falls within the broad field of optimization: stochastic optimization, constrained optimization, mathematical programming, optimization on manifolds, or federated and distributed optimization, using an algorithmic approach. The goal is to deepen the fundamental understanding of the algorithms that are used in practice (convergence analysis, complexity, robustness) and to adapt them to the constraints of large-scale applications (scalability, data heterogeneity, ill-posed problems, as well as communication and memory constraints).

Host laboratories : 

• Centre de recherche en informatique, signal et automatique de Lille (CRIStAL - CNRS/Centrale Lille/Université de Lille)
• Laboratoire des signaux et systèmes (L2S - CNRS/CentraleSupélec/Université Paris-Saclay)
• Laboratoire d’analyse et d’architecture des systèmes (LAAS-CNRS)
• Laboratoire d’informatique de l’École polytechnique (LIX - CNRS/Institut Polytechnique de Paris)

Institutions :

• Institut Polytechnique de Paris (École Polytechnique)
• Université de Lille
• Université Paris-Saclay
• Université Toulouse III - Paul Sabatier

The overall objective of this project is to strengthen the CNRS’s activities in the fields of robotics, control systems, and AI. It aims to develop autonomous systems that interact with humans and are equipped with “embedded intelligence,” enabling them to operate reliably in unstructured and unpredictable environments. Key areas of focus include:

• Developing new control approaches and architectures for autonomous systems (vehicles or robots, including drones) that take human factors into account, including the development of observers and estimators that utilize available data.
• Perceiving and understanding situations in open and dynamic environments, managing uncertainties and ensuring the integrity of the physical system and its environment (operational safety)
• Planning trajectories and spatial mapping (SLAM)

Host laboratories :

• Laboratoire Heuristique et diagnostic des systèmes complexes (Heudiasyc - CNRS/Université de technologie de Compiègne)
• Laboratoire d’automatique, de mécanique et d’informatique industrielles et humaines (LAMIH - CNRS/Université Polytechnique Hauts-de-France)

Institutions :

• Université Polytechnique Hauts de France
• Université de Technologie de Compiègne

To come

Scientific machine learning is undergoing a revolution driven by generative AI, which is capable of learning, often from unsupervised data, universal probabilistic models of the statistical structure of the domains under study. This transforms tasks such as prediction or classification into specific cases of inference, amplified by the power of these models. The challenge lies in extracting interpretable latent variables that reflect the underlying biological mechanisms.
Evolutionary genomics, with its probabilistic models of biological processes (mutations, genome structure, biophysical constraints, selection) and its massive but noisy datasets, offers an ideal testing ground for these advances. Recent progress in AI in biology (AlphaFold, ESM, DNABert) now makes it possible to integrate sequence, structure, function and evolutionary dynamics. Thus, this field naturally draws on emerging ML approaches (simulation-based inference, diffusion models, neural ODEs/SDEs, variational autoencoders) to develop scalable, interpretable inference methods that are accessible to the scientific community. 

Host laboratories :

• Laboratoire de biométrie et biologie évolutive (LBBE - CNRS/Lyon 1 Université/Vetagro Sup)
• Laboratoire interdisciplinaire des sciences du numérique (LISN - CNRS/Université Paris-Saclay)

Institutions :

• Université Claude Bernard Lyon 1
• Université Paris-Saclay

To come