44 Postdoctoral Fellowship at Inria, France

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. 

This project is based on a collaboration between Scool and a medical team at Inserm/CHU de Lille that have been active for 5 years now.

The scientific goal of this collaboration is to investigate the exploitation of data to improve patient follow-up after surgery. We model this problem as a sequential decision making under uncertainty problem.

This collaboration has already produced interesting results, in the form of a website, presentations in conference, and publications in top machine learning conferences, and top medicine journals.

This collaboration involves about 10 people: data engineer, PhD student, post-doc, researchers, professors.

The present postdoctoral project is part of a collaborative project between the Atlantis project-team from the Inria Research Center at Université Côte d’Azur,  (2) Thales R&T in Palaiseau, France, which is the main multidisciplinary research center of Thales group, and one of the main world players in professional electronics (3) and Nanoe in Ballainvilliers, France, whcih is  a SME (Small and Medium Entreprise) specialized  in development and fabrication of innovative ceramic materials for the industries.

Atlantis is  a joint project-team  between Inria and  the Jean-Alexandre Dieudonné Mathematics Laboratory at  Université Côte d’Azur. The team  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 systems of  partial differential equations (PDEs) modelling nanoscale light-matter interaction problems. In this context, the team is  developing  the   DIOGENeS  [https://diogenes.inria.fr/]  software suite,  which  implements  several Discontinuous  Galerkin  (DG)  type methods tailored to the systems  of time- and frequency-domain Maxwell equations  possibly coupled  to  differential  equations modeling  the behaviour of propagation  media at optical frequencies.  DIOGENeS is a unique  numerical   framework  leveraging   the  capabilities   of  DG techniques  for  the simulation  of  multiscale  problems relevant  to nanophotonics and nanoplasmonics.

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of

the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de

Paris .

The centre has 39 project teams , 27 of which operate jointly with Paris-Saclay University and the

Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and

innovation support staff, including 44 different nationalities.

This postdoctoral position is part of the national PEPR (Programme et Equipement Prioritaire de Recherche) PlantAgroEco project, coordinated by Alexis Joly. The PEPR involves several teams from various institutes (Inria ZENITH, CIRAD AMAP,  CIRAD PHIM, CIRAD PBVMT, INRAE ePhytia, INRAE IGEPP, INRAE LISAH, IRD EGCE, IRD IEES, Univ. Paris Saclay, TelaBotanica). The position is funded for 18 months, and will be conducted a Inria Lille – Nord Europe under the supervision of Odalric-Ambrym Maillard. This is a postdoctoral position in Machine Learning, more specifically in Reinforcement Learning.

The Inria center at the University of Lille, created in 2008, is based on two sites: one on the University of Lille campus at La Haute-Borne and one in the midst of the EuraTechnologies entrepreneurial ecosystem.

The Inria center at the University of Lille has 15 project teams. Its activities occupy over 360 people, scientists and research and innovation support staff, including 38 different nationalities. Its scientific priority areas are data science, Software engineering, and cyber-physical systems.

This project follows the line of long term reserach on the development of improved discretizations for complex PDEs. Here the main focus are hyperbolic balance laws arising in many applications in physics and engineering. More particularly this project looks at so called structure preserving methods which  embed genuinely discrete analogs of continuous constraints. Examples   are  solenoidal constraints and curl involutions, which also include he  enhanced preservation of steady states, often referred to as well balanced. Other constraints as non-negativity or bounded variation  within physically admissible values are also of great importance.

This work follows initial activities on the so called global flux quadrature (GFQ) approach, which has been shown to provide great enhancements in the approximation of stationary states, including multiD solenoidal constraints (see e.g. [GFQ-preprint] to appear on J.Comput.Phys., or [GFQ-talk] presented at the Workshop on numerical approximation of hyperbolic PDEs in honor of Prof. Carlos Parés’ 60th birthday )

The postdoc will contribute to the investigation of several possible extensions of the approach, going from its application to more complex PDE systems, to the methodological enhancements discussed below.

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.

The goal of this post-doc is to bridge that gap by developing new methods that enable to model and account for the specificities of a given domain with specialty language, while still benefitting from the models and capabilities developed for the common language.

Le centre de recherche Inria de Paris est l’un des neuf centres de recherche Inria regroupant 700 personnes réparties dans 34 équipes de recherches et 7 services d’appui à la recherche. Il est un acteur majeur et reconnu dans le domaine des sciences numériques en région Ile de France. Ses axes de recherche principaux sont : intelligence artificielle, santé numérique, robotique, fiabilité des logiciels et sécurité, etc.

Il est au cœur d’un riche écosystème de R&D et d’innovation : acteurs de la recherche et de l’enseignement supérieur, laboratoires d’excellence, institut de recherche technologique, PME fortement innovantes, grands groupes industriels, pôles de compétitivité…

A post-doctoral fellowship is available in the Inria Agora team in the Lyon center. This position is funded in the context of the National French PEPR projects on “Networks of the Future” and “Mobility Digitalization”. The post-doctoral fellow will collaborate with Dr. Razvan Stanica (http://perso.citi.insa-lyon.fr/rstanica/) from the Inria Agora team and with Dr. Aline Viana (https://pages.saclay.inria.fr/aline.viana/) from the Inria TRiBE team.

Inria Agora is a joint team with INSA Lyon, one of the most prestigious engineering schools in France. Teaching involvement and student supervision are possible (but not mandatory) at INSA Lyon.

This postdoctoral position is part of the national PEPR (Programme et Equipement Prioritaire de Recherche) PlantAgroEco project, coordinated by Alexis Joly. The PEPR involves several teams from various institutes (Inria ZENITH, CIRAD AMAP,  CIRAD PHIM, CIRAD PBVMT, INRAE ePhytia, INRAE IGEPP, INRAE LISAH, IRD EGCE, IRD IEES, Univ. Paris Saclay, TelaBotanica).  This is a postdoctoral position in Machine Learning, more specifically in Reinforcement Learning. We are seeking a highly motivated and skilled postdoctoral fellow to join the project, dedicated to advancing the field of Machine Learning, with a specific focus on Reinforcement Learning. The position is initially funded for 18-month, but it can be easily extended.

The post-holder will be a member of the DataShape team, and work under the supervision of Nina Otter. 

DataShape is a research team in Topological Data Analysis (TDA), constituted of researchers from Inria-Saclay, Inria Sophia Antipolis, and from the Laboratoire de Mathématiques d’Orsay. TDA is a recent field whose aim is to uncover, understand and exploit the topological and geometric structure underlying complex and possibly high-dimensional data. The DataShape team gathers a unique variety of expertise that allows it to embrace the mathematical, statistical, algorithmic and applied aspects of the field in a common framework ranging from fundamental theoretical studies to experimental research and software development.

 

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Post-Doctoral Research Visit F/M Distributed Machine Learning at the Network Edge

Deadline : 2024-05-31

View details & Apply

 

Inria is the French national research institute for digital science and technology. World-class research, technological innovation, and entrepreneurial risk are its DNA. In 215 project teams, many of which are shared with major research universities, more than 3,900 researchers and engineers explore new paths, often in an interdisciplinary manner and in collaboration with industrial partners to meet ambitious challenges.

As a technological institute, Inria supports the diversity of innovation pathways: from open-source software publishing to the creation of technological startups (Deeptech).

Inria Paris is located near Gare de Lyon in Paris.

Dans le cadre d’un partenariat entre l’équipe projet Inria et Thalès, l’objectif de ce poste est de proposer, de simuler dans un environnement logiciel, puis d’expérimenter sur une plateforme réelle une solution de diffusion pour groupes multicast dans une flotte de drones auto-organisé.

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.

The postdoctoral project is part of a collaboration between Inria, University Côte d’Azur (Roland Masson and Konstantin Brenner) and  IFPEN (Isabelle Faille and Guillaume Enchery) in the framework of the Inria-IFPEN partnership and of the project MathSout of the PEPR MathVives.  

The position will be held at the Laboratoire de Mathématiques J.A. Dieudonné (LJAD), Université Côte d’Azur, Nice, with regular visits at IFPEN, Rueil Malmaison.  

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.

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris since 2021.

The centre has 39 project teams , 27 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris. Its activities occupy over 600 scientists and research and innovation support staff, including 54 different nationalities.

In the framework of parametric model reduction, registration is the process of finding a bijection (or morphing) to align coherent structures of the solution in a reference configuration, over a range of parameters [2]. The problem is tightly linked to point-set registration in image processing, and shares relevant features with mesh morphing (r-adaptation) in scientific computing; nevertheless, registration for model reduction applications has several specificities that require innovative methodological solutions and motivate further research. First, in order to allow the correct enforcement of boundary conditions, the mapping should exactly preserve the boundary of the domain for all parameter values; second, the quality of the deformed mesh should be controlled; third, registration should rely on a moderate number of possibly lowfidelity snapshots to reduce the offline costs.

The objective of the exploratory action AM2OR (www.inria.fr/en/am2or) is to combine mesh adaptation, registration, and model order reduction to devise cost-efficient reduced-order models for parametric advection-dominated systems. In this respect, the importance of registration is twofold: first, to contribute to find a low-rank nonlinear representation of the solution field over a range of parameters; second, to facilitate the task of building a common mesh for all elements of the solution set. The recent publication [1] illustrates an integrated procedure to adaptively build the mesh, the parametric mapping, and the reduced-order model for two-dimensional conservation laws.

The team expertise encompasses mathematical (stochastic) and numerical modelling of ocean flows, observational and physical oceanography, data assimilation and machine learning. Gathering this large panel of skills, the team aims at improving our understanding, reconstruction and forecasting of ocean dynamics, and more specifically to bridge model-driven and observation-driven paradigms to develop and learn novel representations of the coupled ocean-atmosphere dynamics ocean models. The postdoctoral researcher will work with Noé Lahaye (Inria, Rennes), but close collaborations with other members of the team, in particular with Aurélien Ponte (Ifremer), are expected. The postdoc will be based either at Inria (Rennes, France) or at Ifremer (Brest, France) — as he/she pleases.

The postdoc position is funded by the French National Research Agency (ANR JCJC), as part of the ModITO project (Modelling the Internal Tide in the Ocean)

Funding for usual expenses (travelling, computer…) has been secured, including 1 international conference per year.

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 Postdoctoral Research Fellowship position is proposed by the Mathrisk INRIA  Research team, https://team.inria.fr/mathrisk/en/, which is common with INRIA, the Ecole Nationale des Ponts et Chaussées and the University Gustave Eiffel. 

The  Mathrisk team addresses broad research topics in the area of  mathematical handling of risk, embracing risk measurement and risk management, modeling and optimization in quantitative finance and other related domains where risk control is paramount. 

It also develops a numerical platform for quantitative finance(http://www.premia.fr), supported by a consortium of financial institutions.

Mathematical expertise of the team includes stochastic modeling, stochastic analysis, in particular stochastic (partial) differential equations and various aspects of stochastic control and optimal stopping of these equations, stochastic optimization, dynamic game theory, random graphs, martingale optimal transport and numerical probability. In recent years, systemic risk has emerged as a major focus of research. Unlike traditional risk management approaches that primarily consider risks faced by individual institutions, the emphasis now lies on modeling the intricate interrelationships among institutions and the mechanisms of distress propagation across them. Leveraging a multi-agent reinforcement learning (MARL) approach enables a more detailed examination of systemic risk within financial networks. As evidenced by past crises, effective tools for monitoring stability in large and complex financial systems must accurately account for the diverse interconnections within these systems. In this context, our research proposal builds upon our previous findings on systemic risk using a random graph approach, integrating mean-field games and reinforcement learning strategies. This integration enhances both the depth and precision of our analyses within a dynamic financial network framework.

The PULSE challenge, shared by Inria and Qarnot computing, aims to develop and promote best practices in geo-distributed hardware and software infrastructures for intensive computing with a reduced environmental footprint.

The aim of this postdoc is to better quantify the difference in environmental footprint of different models of geo-distributed computing, so as to be able to better manage the induced impacts (according to a multi-criteria approach). Ultimately, we hope to be able to recommend best practice hardware and software architectures to drastically reduce impacts while offering the best quality of service to end-users.

It has been recently recognized that the micrometric scale may play a crucial role to explain cardiac arrhythmia occurring at the organ scale \cite{jaeger2021frommillimetmicromet}. At this scale, the geometry of individual cells, and their spatial organization has to be taken into account, and a correct model is the extracellular, membrane, intracellular (EMI) model described in \cite{tveito2017cellbased}.

It is the aim of the MICROCARD project (ending in September 2024) to build an efficient high-performance computer software to solve the equations of the EMI model. To this aim, we are currently investigating the interest of finite volume methods, and have obtained some results for a standard scheme, adapted to the problem \cite{FVCA10.2023}. The methods studied up to now are restricted to triangular and tetrahedral mesh elements, and are low order approximations.

The variations of the electrical field may occur at a scale close to the cell length (see \cite{jaeger2021frommillimetmicromet}), meaning that the approximation may require mesh elements having the size of the cells. Anyway, the cells have complex geometries, which discretization needs smaller tetrahedrons. We want to investigate how recent virtual elements method \cite{antonietti2022virtualelementmethodapplicat} or polytopal discretizations \cite{dipietro2020hybridhighorder} may be used to discretize the EMI equations with less mesh elements (of more general shape), while retaining their precise geometry, and a good accuracy.

You will be a part of Topal INRIA team in Bordeaux, which includes experts in both HPC and AI fields. 

Particularly, for the last several years members of the Topal team have been working on optimizing the training of neural networks by applying techniques from high performance computing, linear and tensor algebra (please, see papers from ICML’23, ICML’23, IJCAI’22, NeurIPS’21 and workshop WANT@NeurIPS’23). You will work closely with Julia Gusak, Lionel Eyraud-Dubois, and Olivier Beaumont. 

The CAPSID Inria team develops computational approaches for the modeling and docking of nucleic acids (NA = RNA/DNA) binding to proteins. While AI-based methods have revolutionized the modeling of protein complexes, its application to protein-NA complexes has provided mitigated success, and failed in the case of highly flexible single-stranded NA. We have developed a combinatorial approach to build atomic representations of single-stranded RNA bound to a protein [ORCA-3D project] [1]: RNA fragments are docked separately, then reassembled by path search in a graph of compatible poses. Our method is the only one to date capable of providing accurate structural models of ssRNA-protein complexes, using only protein structure and RNA sequence. 

Recent advances in computational mass spectrometry-based metabolomics have unleashed a massive amount of chemical knowledge from metabolomics analysis. Yet, there is a need for a comprehensive computational framework that can better integrate the information derived from both experimental and computational analyses and help chemists analyse their results.

We invite applications for a Postdoctoral Researcher position at the Nice Chemistry Institute (ICN) and the Wimmics team at Inria both hosted at the Université Côte d’Azur. The selected candidate will be jointly embedded and interact with PhD students and research engineers of the Interdisciplinary Institute for Artificial Intelligence (3iA) TechPool, and operates in the context of international collaboration between France and Switzerland. The position is initially offered for one year, with the possibility of extension up to three years.

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.

The objective of this research project is de develop a mathematical model and asociated numerical methods for the simulation of the hemodynamics in the four cavities of the heart. The main motivation of this work is driven by the developement of a simulation tool to simulate patohological scenarios, such as the Hypoplastic Left Heart Syndrome (HLHS), and numerically asses different surgicals options. This work will be performed in the framework of the MEDITWIN project, which involves a collaboration beetwen COMMEDIA projec-team, Dassault Systèmes and SIMBIOTX project-team.

This post-doctoral position will be supported by the Fed-MALIN project.
Fed-MALIN addresses a number of challenges that arise when Federated Learning (FL) is deployed over the Internet, including privacy, fairness, energy consumption, personalisation, and location/time dependencies.
Fed-MALIN will also contribute to the development of open-source tools for FL and will use them for concrete applications in medicine and crowdsensing.

The position is part of a collaboration between two teams of Inria Lille: Spirals (Self-adaptation for distributed services and large software systems Magnet (Machine Learning in Information Networks).

The employment context of this position is the Inria MusMaps exploratory action.

MusMapS exploits effort maps of pathological subjects to personalize the assistance of an upper limb exoskeleton by shared control. Musculoskeletal coupling – shared control for neuromuscular (myopahties) or neurodegenerative (multiple sclerosis, post-stroke, etc.) pathologies is a research challenge with high applicative potential for these patients.

This exploratory project is being carried out jointly by the Inria MimeTIC and Rainbow teams, in collaboration with the Pôle Saint Hélier and the Innovations Handicap Autonomie et Accessibilité (IH2A) chair at INSA Rennes.

Shared control has now become a benchmark in the development of assistance systems (cobots, exoskeletons) designed to partially or fully replace or assist a user’s activity. Shared control refers to a collaborative and cooperative approach in which man and machine contribute to the control of a system. This enables a balance to be struck between human intuition and machine precision. In particular, robotics for the disabled has long been concerned with these issues, which require both a good interpretation of the user’s intentions and the generation of a control adapted to the needs of the identified task and the user. In this project, we will be focusing on upper limb assistance systems. Many shared control strategies are based on admittance control [4]. Common applications in the context of disability are assistance with everyday tasks, such as grasping an object, opening a door, drinking… Patients suffering from neuromuscular pathologies (myopathies) or neurodegenerative diseases (Parkinson’s, multiple sclerosis, age-related muscle weakness, etc.) experience a reduction in their capacity for generations of effort, but also potentially in their motor control. This individual variability means that the assistance system needs to be customized according to the level of impairment, through several operating “modes”.

The INRIA team Gamble is part of the INRIA Nancy Grand Est research center and also of the department Algorithms, Computation, Geometry and Image of the LORIA laboratory of Université de Lorraine.

Classical computational geometry usually deals with linear objects in a Euclidean setting and when other situations happen, curved objects are typically linearized and non-Euclidean spaces are locally approximated by Euclidean spaces. The goals of the Gamble team are to address such limitations of classical computational geometry.

The Inria research centre in Lyon is the 9th Inria research centre, formally created in January 2022.  It brings together approximately 300 people in 16 research teams and research support services.

Its staff are distributed at this stage on 2 campuses: in Villeurbanne La Doua (Centre / INSA Lyon / UCBL) on the one hand, and Lyon Gerland (ENS de Lyon) on the other.

The Lyon centre is active in the fields of software, distributed and high-performance computing, embedded systems, quantum computing and privacy in the digital world, but also in digital health and computational biology. 

Inria Défense&Sécurité (Inria D&S) a été créé en 2020 pour fédérer, de façon la plus lisible et opérationnelle possible, les différentes actions d’Inria pouvant répondre aux besoins numériques des forces armées et forces de l’intérieur. L’ambition affichée par Inria est de produire de la recherche ainsi qu’un ensemble de technologies scientifiquement fondées, susceptibles d’être utilisées pour répondre aux défis imminents de la sphère Sécurité Défense.

Inria D&S souhaite développer ses activités de recherche autour du traitement automatique du langage pour des cas d’usage répondant spécifiquement aux besoins dans le domaine Défense&Sécurité. Ces recherches sont menées à travers des collaborations avec le Ministère des Armées et portent sur différentes facettes du domaine comme la transcription de la parole, la traduction automatique, la détection d’entités nommées, la détection de deep-fake et l’anonymisation/certification d’origine de la parole ou, encore, la reconnaissance du locuteur et de la langue parlée.

Inria Défense&Sécurité (Inria D&S) a été créé en 2020 pour fédérer, de façon la plus lisible et opérationnelle possible, les différentes actions d’Inria pouvant répondre aux besoins numériques des forces armées et forces de l’intérieur. L’ambition affichée par Inria est de produire de la recherche ainsi qu’un ensemble de technologies scientifiquement fondées, susceptibles d’être utilisées pour répondre aux défis imminents de la sphère Sécurité Défense.

Inria D&S souhaite développer ses activités de recherche autour du traitement automatique du langage pour des cas d’usage répondant spécifiquement aux besoins dans le domaine Défense&Sécurité. Ces recherches sont menées à travers des collaborations avec le Ministère des Armées et portent sur différentes facettes du domaine comme la transcription de la parole, la traduction automatique, la détection d’entités nommées, la détection de deepfake et l’anonymisation/certification d’origine de la parole ou, encore, la reconnaissance du locuteur et de la langue parlée.

Inria Défense&Sécurité (Inria D&S) a été créé en 2020 pour fédérer, de façon la plus lisible et opérationnelle possible, les différentes actions d’Inria pouvant répondre aux besoins numériques des forces armées et forces de l’intérieur. L’ambition affichée par Inria est de produire de la recherche ainsi qu’un ensemble de technologies scientifiquement fondées, susceptibles d’être utilisées pour répondre aux défis imminents de la sphère Sécurité Défense.

Inria D&S souhaite développer ses activités de recherche autour du traitement automatique du langage pour des cas d’usage répondant spécifiquement aux besoins dans le domaine Défense&Sécurité. Ces recherches sont menées à travers des collaborations avec le Ministère des Armées et portent sur différentes facettes du domaine comme la transcription de la parole, la traduction automatique, la détection d’entités nommées, la détection de deepfake et l’anonymisation/certification d’origine de la parole ou, encore, la reconnaissance du locuteur et de la langue parlée.