30 Postdoctoral Fellowship at Inria, France

Le centre de recherche Inria Saclay – Île-de-France, créé en 2008, accueille 450 scientifiques et 60 membres des services d’appui à la recherche. Les scientifiques sont organisés en 28 équipes de recherche dont 23 sont communes avec des partenaires du plateau de Saclay. Le centre Inria Saclay – Île-de-France est un acteur essentiel de la recherche en sciences du numérique sur le plateau de Saclay. Il porte les valeurs et les projets qui font l’originalité d’Inria dans le paysage de la recherche : l’excellence scientifique, le transfert technologique, les partenariats pluridisciplinaires avec des établissements aux compétences complémentaires aux nôtres, afin de maximiser l’impact scientifique, économique et sociétal d’Inria.

This post-doctoral position will be supported by the HE Flute 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 goal of the multi-disciplinary FLUTE project is to advance and scale up data-driven healthcare by developing novel methods for privacy-preserving cross-border utilization of data hubs. Advanced research will be performed to push the performance envelope of secure multi-party computation in Federated Learning, including the associated AI models and secure execution environments. The INRIA MAGNET team (and hence the recruited collaborators) will contribute to this project among others by researching machine learning algorithms and multi-party protocols with improved scalability in the context of medical data, e.g., by exploiting data sparsity.   This research will involve both theoretical and more applied components.  As coordinator INRIA will also contribute to the integration of the software developed in the FLUTE project (and the complementary FLUTE project).

Avec l’aide de Bertrand Thirion et Judith Abécassis, la personne recrutée sera amenée à mettre au point des estimateurs de médiation utilsiant des outils avancés de machine learning.

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.

The project will be held in the TARAN (formerly CAIRN) team of the IRISA/INRIA laboratory. The TARAN team, with more than 35 members from Inria, UR1, and ENS Rennes, has participated in several national and European R&D projects (H2020 ARGO, FP7 Alma, FP7 Flextiles) and has strong industrial collaborations (e.g., Safran, Thales, Alcatel, Orange, STMicroelectronics, Technicolor, and various SMEs). TARAN has recognized experience in several domains related to the project, such as embedded system design, fault tolerance, safety-critical systems, computing architectures, design tools for specialized hardware architectures.

We’re looking for someone with experience in neuroimaging of social cognition who can contribute meaningfully to this new era of hyperscanning research. Particularly, we are seeking someone who can assist in the design of rigorous naturalistic hyperscanning paradigms which investigate social phenomena such as collaboration, rapport, learning, and performance, and the analysis of the resulting data, preferably using newer machine learning analytic tools. In addition to investigating understudied phenomena of social cognition, these findings will eventually be utilized to improve a virtual peer – a conversational agent that builds bonds with children in the service of learning. The project will involve investigating the neural mechanisms underlying social cognition in real-time interactions of child peer dyads aged 5-12 years old using fNIRS hyperscanning. The ideal candidate will have experience with fNIRS data acquisition and analysis, as well as strong programming skills in Python and so experience in developmental psychology, cognitive science, learning science, and/or linguistics will be an asset.

Participation in the “Investigation of potential biomarkers to detect chronic inflammation in Multiple Sclerosis through diffusion MRI”, a project funded within the Biomedical Engineering Seed Grant Program by the Fondation Bettencourt Schueller.

Ce sujet concerne la sécurité des données personnelles dans le cadre d’applications citoyens cherchant à analyser de grands volumes de données personnelles. Spécifiquement, le projet est focalisé sur le big data décentralisé et sécurisé, avec comme objectif, le calcul sur des données personnelles fournies par un très grand nombre d’individus, pour permettre, par exemple, la classification automatique d’écritures bancaires. L’équipe Petrus travaille sur la problématique du cloud personnel sécurisé à base de matériel sécurisé côté utilisateur, à savoir, le développement d’une plateforme permettant à tout individu de rapatrier, stocker, gérer, utiliser et partager d’une manière sécurisée l’ensemble de ses données personnelles.

The Inria centre at Université Côte d’Azur includes 37 research teams and 8 support services. The centre’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d’Azur, CNRS, INRAE, INSERM …), but also with the regiona economic players. With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d’Azur  is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

This post-doc position fits within the roadmap activities of Inria’s Defense & Security Department, which is devoted to applications-driven research. Among the various fields of NLP, information extraction is a crossroad topic that, by focusing on how to turn raw documents into structured data models, echoes the practical needs of many end-users in a broad range of sectors. Information extraction components such as entity recognition or relation extraction are thus key to a number of industrial and general-public applications. However, whereas information extraction has seen major progresses in the last few years on common language (Wikipedia, news, everyday language), it still lags behind on specialty language, which effectively affects a number of practical applications. Main challenges include unknown words and concepts, unusual phrasings, or differences in the nature of information that is interesting to extract.

 

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Post-Doctoral Research Visit F/M Numerical modeling of the impact of blood flow on the development of cerebral vasculopathy

Deadline :2023-11-30

View details & Apply

 

The Centre Inria de l’Université de Grenoble groups together almost 600 people in 22 research teams and 7 research support departments. Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area. The Centre Inria de l’Université Grenoble Alpe is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

This Post-doc subject is proposed in the context of the ROAD-AI Challenge involving the Cerema and the Inria Team ACENTAURI. ACENTAURI is a robotic team located in Sophia Antipolis that studies and develop intelligent, autonomous and mobile robots that collaborate between them to achieve challenging tasks in dynamic environments. The team tackle perception, decision and control problems for multi-robot collaboration by proposing an original hybrid model-driven/data driven approach to artificial intelligence and by studying efficient algorithms. The team focus on robotic applications like environment monitoring and transportation of people and goods. In these applications, several robots will share multi-sensor information eventually coming from infrastructure. The effectiveness of the proposed approaches are demonstrated on real robotic systems like cars AGVs and UAVs together with industrial partners. Non Destructive Evaluation of Structures and Materials (ENDSUM), directed by Cyrille FAUCHARD, has for main objective to contribute to the development of tools and methods of characterization and diagnosis with great efficiency, less invasive than the current tools and not altering the environments auscultated to meet the challenges of Asset Management of Infrastructures (IPM). The research carried out is aimed first of all at the physical and geometric characterization of environments using a multi-parameter, multi-scale and multi-physical approach linking the quantities measured to indicators useful for the diagnosis and monitoring over time of the environments observed. ENDSUM also aims at the development of analytical and numerical modeling, inversion and data processing and analysis methods (signals, images) allowing access to the desired physical and geometric properties. Finally, ENDSUM aims to develop innovative prototype software and hardware as well as tools for training, research and expertise.

This position is funded directly by Inria within the project DATA4US and will be hosted in the Privatics team at Sophia Antipolis and led by Nataliia Bielova. The Privatics team is an Inria research group based in Grenoble, Lyon and Sophia Antipolis. Privatics follows a multidisciplinary approach in considering the scientific and technical issues, but also economic, legal and social aspects of privacy. The team has expertise in the identification of privacy issues, anonymization techniques and sanitization database and design of Privacy Enhancing Technologies (PETs). Privatics has a long history of contributing to Standards Developing Organisations (IETF and IEEE), to the public consultations of the European Data Protection Board (EDPB) and EU authorities (such as Data Protection and Consumer Protection) and has a strategic partnership with the French Data Protection Authority (CNIL).

Inria, l’institut national (public) de recherche en sciences et technologies du numérique, s’assume au travers de son contrat d’objectifs et de performance 2019-2023 comme un outil de la souveraineté et de l’autonomie stratégique numérique de la Nation. Le renforcement des partenariats avec la sphère Sécurité-Défense de l’État est une priorité stratégique. Dans ce contexte, Inria est en train de créer un Département Défense et Sécurité dont la mission est de fédérer, de façon la plus lisible et opérationnelle possible, les différentes actions d’Inria pouvant répondre aux besoins numériques de la sphère Défense et Sécurité.

This post-doc position fits within the roadmap activities of Inria’s Defense & Security Department, which is devoted to applications-driven research. Among the various fields of NLP, information extraction is a crossroad topic that, by focusing on how to turn raw documents into structured data models, echoes the practical needs of many end-users in a broad range of sectors. Information extraction components such as entity recognition or relation extraction are thus key to a number of industrial and general-public applications. However, whereas information extraction has seen major progresses in the last few years on common language (Wikipedia, news, everyday language), it still lags behind on specialty language, which effectively affects a number of practical applications. Main challenges include unknown words and concepts, unusual phrasings, or differences in the nature of information that is interesting to extract.

IsoGeometric Analysis (IGA) offers a powerful framework for the numerical analysis of complex geometric structures, but it comes with significant challenges, particularly in domain parameterization. The quality of parameterization significantly influences downstream simulations and other processes. In the IGA-based pipeline, the efficiency of constructing analysis-suitable parameterization is crucial and seen as a bottleneck. This is especially true in the context of 3D geometries.

HEKA and SISTM are collaborating in the context of the new national initiatives to support the digital transformation of medical practices. In this context, the objectives of the HEKA-SISTM collaboration are to propose efficacy and safety/risk assessment via clinical evaluations of AI based DMDs aiming at diagnostics, therapeutics, monitoring patient’s disease, managing patient’s care organization and improving prevention and healthcare.

Wave propagation phenomena are ubiquitous in our modern society and manifest themselves in various contexts such as telecommunications, imaging techniques (medical, seismic), information processing (photonics), energy harvesting and health (biomedical applications). Numerical modeling plays an increasing role in understanding complex wave propagation phenomena or for mastering the wave interactions with our environment. Many numerical methods have been designed for the numerical solution of wave PDE models for unsteady or harmonic propagation regimes, i.e., for time-domain and frequency-domain formulations of these models. In particular high order discontinuous finite element methods, named as Discontinuous Galerkin (DG) methods or Hybridized DG (HDG) have shown significant benefit in terms of accuracy and have become popular. The resulting discretized problem reduces to the solution of a large sparse linear system of equations.

The team LARSEN has been collaborating with the Hospital of Nancy for deploying assistive robotics technologies to help the medi-cal staff in different hospital departments. In the project ExoTurn (https://members.loria.fr/SIvaldi/projets/exoturn-2020/) 4 exo-skeletons were deployed in the Intensive Care Unit to assist the medical staff in the prone positioning maneuver of COVID patients.The medical staff is eager to adopt new solutions to assist the medical staff in other activities and departments, where the ergonom-ics conditions of some workstations are difficult. For this reason, the team has now launched the project ExoSim, to help identifying the activities that can be assisted with exoskeletons.

Networks such as modern telecommunications networks or distributed embedded systems are permanently monitored to allow identification of failure situations; thousands of new data points reflecting the system state changes are generated every minute. Even if faults are rare events, they can easily propagate driven by local and remote dependencies, which makes it challenging to distinguish causes from effects among the thousands of highly correlated alerts. A timely automated identification and root cause analysis (RCA) of the origins of performance issues allows executing the most adequate corrective actions and preventing their further propagation. In general, RCA is a hard problem, because it requires a deep knowledge of cause-effect dependencies among many features, physical and logical components the network nodes. In a data driven approach, where most of this knowledge is unavailable a priori, a major difficulty emanates from hidden or unknown variables. Furthermore, even in a fully observable system we are faced with the combinatorial explosion of potential cause-effect dependencies and the difficulty to collect enough information for distinguishing causality from spurious correlations.

This project is established in the framework of a consortium with technologists, physiciens, biologists and statisticians. It aims to develop a breakthrough approach for the diagnosis and evaluation of cancer metastasis from blood samples by the first smart MEMS (Micro-Electro-Mechanical-Systems) equipment performing high content biophysical characterization of cells in flow for their identification by statistical learning.