25 Postdoctoral Fellowship at Inria, France

Atlantis is  a joint project-team  between Inria, CNRS and  Université Côte d’Azur, which gathers applied mathematicians and  computational scientists who are collaboratively undertaking  research activities aiming at the design, analysis, development and  application of innovative numerical methods for studying nanoscale light-matter interaction problems. In the recent years, the team has  developed the   DIOGENeS  [https://diogenes.inria.fr/]  software suite, which is organized around several numerical tools for the simulation  of physical problems related to the fields of nanophotonics and nanoplasmonics. In particular, this  software suite implements several high-fidelity fullwave solvers based on high-order Discontinuous  Galerkin  (DG)  methods tailored to the systems  of time- and frequency-domain Maxwell equations  possibly coupled  to  differential  equations modeling  the behavior of propagation  media at optical frequencies. Moreover, DIOGENeS also includes algorithms and workflows for the inverse design of nanostructures and nanophotonic devices for harvesting and shaping nanoscale light-matter interactions.  The numerical methods currently implemented in DIOGENeS are accurate and flexible but they are also time consuming. For this reason, the team has recently launched a line of research aiming at the design of novel AI-based methods by considering purely data-driven or model-driven modeling approaches.

The position is in the framework of dAIEDGE—A network of excellence for distributed, trustworthy, efficient and scalable AI at the Edge—funded by the European Union.

The vision of the dAIEDGE Network of Excellence is to strengthen and support the development of the dynamic European edge AI ecosystem under the umbrella of the European AI Lighthouse and to sustain the advanced research and innovation of distributed AI at the edge as essential digital, enabling, and emerging technology in an extensive range of industrial sectors.

This post-doctoral position will be supported by the  HE Trumpet project, the HE Flute project and/or the PEPR IA Redeem project.      While this position will be in the MAGNET team in Lille, we will collaborate with the several European project partners.

While AI techniques are becoming ever more powerful, there is a growing concern about potential risks and abuses. As a result, there has been an increasing interest in research directions such as privacy-preserving machine learning, explainable machine learning, fairness and data protection legislation.
Privacy-preserving machine learning aims at learning (and publishing or applying) a model from data while the data is not revealed. Notions such as (local) differential privacy and its generalizations allow to bound the amount of information revealed.

 The MAGNET team is involved inthe related TRUMPET, FLUTE and REDEEM projects, and is looking for team members who can in close collaboration with other team members and national & international partners contribute to one or more of these projects.  All of these projects aim at researching and prototyping algoirhtms for secure, privacy-preserving federated learning in settings with potentially malicious participants.  The TRUMPET and FLUTE projects focus on applications in the field of oncology, while the REDEEM project has no a priori fixed application domain.

 The start and end date of the offered post-doctoral positions can be negotiated, subject to the administrative constraints that they start at the earliest on 1/5/2024 and end before or around 30/04/2026 and that individual contracts last no longer than 2 years. 

The research project will explore quantum protocols based on the concept of quantum nonlocality and quantum networks (see arXiv:2104.10700). A non-exhaustive list of potential projects is:

• Methods for characterizing quantum correlations beyond the Bell scenario:
  • mathematical foundation of these methods (C* algebras, noncommutative polynomial optimization): see e.g. arXiv:2210.09065, arXiv:2212.11299, arXiv:2301.12513
  • improve/find new algorithms for characterizing these correlations
  • numerical developpement of these algorithms, see e.g. arXiv:2211.04483
• Understanding the foundational implications of quantum correlations in networks, see e.g. arXiv:2101.10873 and arXiv:2105.09381
• Develop the applications of network nonlocality to certification protocols, such as
  • randomness generation: arXiv:2209.09921 
  • self testing of measurements and states: arXiv:1807.04956, arXiv:2201.05032
• Adapt existing protocols for their experimental implementation
• Develop practical benchmarks of the concept of ‘Genuine Multipartite Nonlocality’ introduced in arXiv:2105.09381
• Develop SDP relaxations for condensed matter problems, see e.g. arXiv:2212.03014, arXiv:2310.05844, arXiv:2311.18707, arXiv:2311.18706 
• Explore the limits of quantum distributed computing, see e.g. arXiv:1810.10838, arXiv:0903.113

Athletes face significant physical and mental stress during training and competition, which can hinder performance and threaten long-term health.

Developing reliable, objective tools for stress assessment remains a major challenge, due to the complexity and individual specificity of stress responses.

The ANR project 3CI brings together an interdisciplinary team to develop robust stress indicators by combining multimodal physiological and neural recordings (EEG, fNIRS, ECG, blood pressure, and PPG) with advanced signal processing, mathematical modeling, and artificial intelligence.

This project aims to provide new insights into brain-heart interactions and to enable reliable stress monitoring strategies for athletes.

 We invite applications for a two-year postdoctoral position starting in January 2026!

The successful candidate will join the BOOST project team at Inria Paris-Saclay and contribute to two central work packages of the project:

Integrating signal analysis and artificial intelligence for characterizing brain-heart interplay and defining relevant stress indicators.

Developing robust mathematical and computational models of the brain-heart interaction under stress, combining physiological modeling, dynamical systems theory, and data-driven methods.

The postdoc will work in close collaboration with an interdisciplinary group of researchers and students in control theory, signal processing, neuroscience, and sports science.

Scientific context. This postdoctoral project is part of the JCJC ANR funded project CoYoKi (PI: F. Dégeilh). CoYoKi addresses the methodological challenges of studying brain development and concussion in early childhood (i.e., uniqueness of young child’s brain, individual variability of brain development and injury). It combines cutting-edge advanced neuroimaging, computational and longitudinal statistical models to propose novel individualised and longitudinal neuroimaging methods. To study deviations in brain development, it is essential to understand typical development and its individual variability, a knowledge still lacking, particularly in early childhood due to the absence (until recently) of sufficiently large datasets. This postdoctoral projet will address this gap of knowledge throughout 2 specific aims : 1) Computing a longitudinal atlas of early brain development. A new algorithm will be developed to compute the first longitudinal brain atlas extending from 0 to 5 years. 2) Modelling regional growth charts of early brain structure development. Computational and longitudinal models will be coupled to model the typical trajectories of early brain structure development and their individual variability at the brain regions level.

Main activities : 

• Develop and implement algorithms to find quantum error correcting codes with error correction capabilities adapted to physical noise model 
• Develop and implement error correction algorithms for such codes
• Using convex optimization methods determine fundamental limits for the achievable error correction abilities for a given geometry of interactions and noise model

Additional activities depending on progress : Use proof assistants to transform the numerical fundamental limits into formal proofs

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Post-Doctorant F/H Analyse de modèles probabilistes pour les services d’urgence

Deadline :2025-10-31

View details & Apply

The recruited postdoctoral researcher will contribute to the study of foundational and algorithmic questions related to the extension of OBDA. In particular, such extensions will be motivated by a case study in digital agriculture.

The extension of OBDA towards more expressive queries will consider features among aggregation, navigation, and negation. In this context, a theoretical analysis of semantics, as well as decidability and complexity for query extensions, shall be studied.

The candidate will also contribute to the development of novel frameworks for qualitative and quantitative explanations of query results, with special attention to the interaction between expressivity and explanation, especially when dealing with large sets of explanations to enumerate.

Nous cherchons à recruter un chercheur postdoctoral afin de renforcer les capacités de l’équipe sur l’étude mathématique des systèmes quantiques ouverts. La recherche pourra s’effectuer en collaboration avec plusieurs membres permanents de l’équipe. L’équipe QUANTIC est un partenariat théorie-expérience sur la technologie quantique à base de circuits supraconducteurs, fournissant un contexte clair et riche pour ces recherches théoriques. Les résultats pourront ainsi servir à l’amélioration des technologies quantiques de pointe.  

Le séjour postdoctoral compotera des participations aux séminaires locaux, nationaux, et conférences internationales. Les frais de déplacements seront pris en charge dans la limite du barème en vigueur.

The neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s (PD) disease are the consequences of pathological processes that begin decades before the onset of the typical clinical symptoms [1][2]. However, current diagnosis comes quite late in the course of the disease, while evidences underline the multiple benefits that would be associated with earlier diagnosis [3]. An outstanding challenge for clinical neurosciences is therefore to provide reliable, non-invasive, affordable and easy-to-track biomarkers able to improve both the early detection and the monitoring of neurodegenerative diseases, that can be applied at an individual level. It is well acknowledged that AD and PD display a progressive multifactorial disruption of cerebral networks, all along the course of the diseases, which is highly related to the clinical phenotype [4].

In the search for those biomarkers, the introduction of non-invasive imaging techniques, such as functional magnetic resonance imaging (fMRI) and diffusion weighted imaging (DWI), prompted important discoveries to provide a comprehensive map of neural connections, known as the connectome. The field of network science for analyzing the connectome offers new insights into networks disruptions that are characteristic of specific brain disorders [5]. Mathematical modelling using graph theory, which appeared in neuroimaging at the beginning of this century, provides powerful quantitative tools and measures for the analysis of complex cerebral networks [6][7]. Undirected brain connectivity has been classified in two categories: (i) structural connectivity estimated by DWI, where links represent axons or neuronal fiber density or (ii) functional connectivity (measured for instance with fMRI) where links represent statistical dependencies between brain signals from different areas, such as correlations, coherence, or transfer entropy. However, prior studies have largely focused on the comparison between patients suffering from AD or PD versus healthy subjects. As a result, the relevance of the reported alterations in brain network may be limited due to a lack of specificity. Indeed, the extracted features that are sensitive to AD or PD may well reflect common neurodegenerative processes, therefore lacking specificity for the disease-related physiopathology at the individual level. Integrating simultaneously these modalities could yield a powerful tool, to expand the knowledge of our brain and to exhibit robust biomarkers of AD and PD, more sensitive to pathophysiological changes.

Il est de développer une méthode d’estimation du connectome à partir des enregistrements simultanés EEG-fMRI.  Des différences dans les matrices de connectivité mesurées par EEG et IRMf sont attendues en raison des différences de résolution spatiale et temporelle mais aussi parce qu’elles capturent différents mécanismes de l’activité cérébrale. Une forte corrélation intermodale a été trouvée dans la bande de fréquence EEG-β.

La personne recrutée devra estimer les informations de connectivité communes et complémentaires et la relation entre l’organisation de la connectivité EEG et IRMf dans différentes bandes de fréquences, en utilisant la connectivité dynamique [5]. Cela sera fait en collaboration avec Jonathan Wirsich, Université de Genève, Genève, qui fournira une expertise dans l’estimation de la connectivité dynamique EEG. Le postdoc devra développer une approche

Exploiter les caractéristiques complémentaires et similaires avec une approche de fusion appropriée est crucial pour améliorer l’estimation du connectome et identifier de nouveaux biomarqueurs des maladies. Dans ce contexte, le postdoctorant devra construire des graphes multicouches contenant des matrices de connectivité IRMf et EEG.

Within the framework of the FRESCO ERC project, a package is specifically dedicated to the study of proof transport methodologies and tools in type theory.

The purpose of this project is to investigate how interactive provers based on type theory (Rocq, Agda, Lean, etc.) can be turned into an instrument for producing and checking computer-aided mathematics. The agenda of the present position is to provide a better understanding of how to embody category theory and abstract algebra in type theory, in practice.