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32 PhD Degree-Fully Funded at Inria, France

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Inria, France invites online Application for number of  Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Inria, France.

Eligible candidate may Apply as soon as possible.

 

(01) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Hair Capture and Modeling

The Inria Grenoble research center groups together almost 600 people in 23 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 (University Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area. Inria Grenoble 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.

Deadline :2023-10-31

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(02) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Learning bimanual robot skills from human demonstrations and natural language

The team LARSEN is involved in the European project euROBIN. One of the main goals of the project is to advance cognition-enabled transferable embodied AI. Scientifically, it will substantially advance four core scientific topics: InterAct, Learning transfer, Transferable knowledge, and Human-center transfer. Three robotics domains are investigated: manufacturing, outdoor and personal robotics. In this project, Inria is leading the personal robotics challenge. In this project, INRIA is leading the personal Robotics Challenge, where bimanual manipulators and humanoid robots must execute a variety of complex manipulation, navigation and interaction tasks in a household scenario. Some of these tasks involve unloading a dishwasher, opening a fridge to take an object, folding clothes, carrying and handing objects to humans.

Deadline : 2023-10-31

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(03) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Verified Offloading Orchestration of Network Functions at the Edge

This work is in the context of the HiSec project. The HiSec project is part of the 5G PEPR founded by the ANR, which focuses on cyber-security issues in future networks. These networks have played a key role in service delivery for digital infrastructures. These new networking technologies have also penetrated essential and critical services for our daily lives, such as energy, transportation or healthcare. The pervasive use of digital services and networks to control these critical infrastructures significantly increases the attack surface and the opportunities for attackers. We regularly observe attacks against these infrastructures, leading to successful compromise and very significant impacts. The objective of the HiSec project is thus to handle cybersecurity issues in these environments, and propose new mechanisms to protect these networks and detect attacks, attacks against the networking infrastructure itself, or against the services hosted or the users of the network.

Deadline : 2023-10-31

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(04) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Topology-aware load balancing for ocean simulation on heterogeneous platforms.

The Inria center at the University of Bordeaux is one of the nine Inria centers in France and has about twenty research teams.. The Inria centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute…

Deadline : 2023-11-01

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(05) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M PhD fellowship F/M Methods and models for the clinical evaluation of Digital Medical Devices

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.

Deadline : 2023-11-05

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(06) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M PhD Position F/M Responsible AI with Reinforcement Learning under Constraints

The Inria Lille – Nord Europe research centre, created in 2008, has a staff of 360, including 305 scientists in 15 research teams. Recognised for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria Lille – Nord Europe research centre pursues a close relationship with large companies and SMEs. By promoting synergies between researchers and industrialists, Inria participates in the transfer of skills and expertise in digital technologies and provides access to the best European and international research for the benefit of innovation and companies, particularly in the region. For more than 10 years, the Inria Lille – Nord Europe centre has been located at the heart of Lille’s university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on Avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT).

Deadline : 2023-11-06

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(07) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Open PhD grant available at Inria Grenoble on “AI-driven safe motion planning & driving decision-making for autonomous driving”

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.

Deadline :  2023-11-10

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(08) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Learning Sequences of Contact States for Object Manipulation

The Inria Grenoble research center groups together almost 600 people in 23 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 (University Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area. Inria Grenoble 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.

Deadline : 2023-11-12

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(09) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Combinatorial Optimization with GNNs

Combinatorial optimization (CO) is a field of computer science and mathematics with many significant applications in the real world. They consist of optimizing a specific cost function in a finite collection of objects, for example, a selection of edges to form a cycle in a graph. Many prominent CO problems (including max clique and traveling salesman problem) were classified in the NP class, posing them as computationally intractable for large instances. Since then, many improvements have been made to either finding optimal solutions, good heuristics, or approximate solutions. However, in many practical situations, one often needs to solve problem instances that share particular characteristics or patterns. Hence, machine learning approaches could develop faster algorithms for practical cases by exploiting common patterns in the given instances. In this thesis, we will explore new methods by first studying three different CO problems: maximum clique, traveling salesman problem, and minimum bisection.

Deadline : 2023-11-15

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(10) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Constructing Cyber Security Knowledge Encoding and Reasoning from Heterogeneous Sources with Large Language Models

The Inria Centre at Rennes University is one of Inria’s eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

Deadline :  2023-11-15

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(11) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Privacy on-demand and Security preserving Federated Generative Networks or Models

Future sixth-generation (6G) networks will be highly heterogeneous, with the massive development of mobile edge computing inside networks. Furthermore, 6G is expected to support dynamic network environments and provide diversified intelligent services with stringent Quality of Service (QoS) re- quirements. Various new intelligent applications and services will emerge (including augmented reality (AR), wireless machine interaction, smart city, etc) and will enable tactile communications and In- ternet of everything (IoE). This will challenge wireless networks in the dimensions of delay, energy consumption, interaction, reliability, and degree of intelligence and knowledge, but also in the dimen- sion of information and data sharing. In turn, 6G networks will be expected about leveraging data at the next step of the new communication system generation. First of all, they will generate large amounts of data much more data than 5G networks: multiple sources as Core, Radio Access Network, OAM, User Equipments (UEs) but also as private and/or personal devices/machines massively con- nected, data-generator applications as sensing, localization, context-awareness services etc. Besides, unlike today’s networks where traffic is almost entirely centralized, most 6G traffic will remain localized and highly distributed. The communication system will not only provide the bits reliably, but more importantly will provide the intelligent data processing through connectivity and resources computing in the devices, the edge, and the cloud in the network. For this, with Artificial Intelligence (AI) and Machine Learning (ML), machines will bring to networks the necessary intelligence very close to the place of action and decision-making and will also make data sharing possible. 

Deadline : 2023-11-19

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(12) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Exploring the variability induced by different configurations in the neuroimaging analytical space

The goal of this fellowship will be two-folds: 1/ to provide the first complete model of the task-fMRI analytical space in which analytical pipelines are represented as different configurations and 2/ to explore the analytical space to investigate the main sources of variability. This model will be tested against large-scale real datasets such as NARPS (Botvinik-Nezer et al., 2020) and the Human Connectome Project (Van Essen et al., 2013).

Deadline :  2023-11-19

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(13) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Sharing FAIR protocols and workflows to better understand analytical variability in neuroimaging

The selected fellow will: 1/ Identify protocols and workflows associated with large open neuroimaging datasets (see below for an initial list of datasets to be considered), 2/ share workflows and protocols using the standard and best practices developed in ShareFAIR, 3/ Explore neuroimaging workflows and protocols to identify patterns of interest (e.g. which pipelines are most-widely used by the community).

Deadline : 2023-11-19

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(14) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Sensors-based Control of an Aerial Manipulator for Complex Manipulation of Articulated Objects

Researchers are trying to make aerial robots perform physical work for new applications like construction, inspection, maintenance, etc. Current methodologies show promising results, but they are limited to very simple tasks only performed in lab environments. In this Thesis we want to go beyond this limited scenario. By the investigation of new sensors-based control methods, we want to make aerial manipulators able to perform much complex tasks using onboard sensors only, especially considering manipulation of articulated objects.

Deadline :  2023-11-21

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(15) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Signal processing-based on the squared eigenfunctions of the Schrodinger operator. Application to EEG signals

The objective of this research project is to study the mathematical properties of the SCSA and compare the approach qualitatively and quantitatively to state-of-the-art methods such as Wavelets and empirical mode decomposition methods. A parallel between quantum and signal processing properties will be studied. In particular, the student will use some quantum properties of the operator to propose new criteria for the selection of the semi-classical parameter a key design parameter of the method for both denoising and signal characterization. Numerical properties of the algorithm will be also analyzed, with a focus on the optimization of the current codes. Applications to biomedical signals will be investigated with a particular focus on EEG signals for epileptic seizure detection and prediction and for monitoring the mental health of athletes.

Deadline : 2026-09-30

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(16) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M PhD position F/M Building physics-based multilevel surrogate models from neural networks. Application to electromagnetic wave propagation

Numerical simulations of electromagnetic wave propagation problems primarily rely on a space discretization of the system of Maxwell’s equations using methods such as finite differences or finite elements. For complex and realistic three-dimensional situations, such a process can be computationally prohibitive, especially when the end goal consists in many-query analyses (e.g., optimization design and uncertainty quantification). Therefore, developing cost-effective surrogate models is of great practical significance. There exist different possible ways of building surrogate models for a given system of partial differential equations (PDEs) in a non-intrusive way (i.e., with minimal modifications to an existing discretization-based simulation methodology). In recent years, approaches based on neural networks (NNs) and Deep Learning (DL) have shown much promise, thanks to their capability of handling nonlinear or/and high dimensional problems. Model-based neural networks, as opposed to purely data-driven neural networks, are currently the subject of intense research for devising high-performance surrogate models of parametric PDEs.

Deadline : 2024-11-30

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(17) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Fast solvers for studying light absorption by nanostructured imagers

The exploitation of nanostructuring in order to improve the performance of CMOS imagers based on microlens grids is a very promising avenue. In this perspective, numerical modeling is a key component to accurately characterize and optimize the absorption properties of these complex imaging structures which are intrinsically multiscale (from the micrometer scale of the lenses to the nanometric characteristics of the nanostructured material layers). The present PhD project is proposed in the context of a collaboration between the Atlantis project-team of Inria research center at Université Côte d’Azur and STMicrolectronics (CMOS Imagers division of the Technology for Optical Sensors department) in Crolles. A Cifre funding will support this project.

Deadline :2024-06-30

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(18) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M PhD student on federate learning and multi-party computation techniques for prostate cancer

The recruited PhD student will collaborate with colleagues in the MAGNET team and the FLUTE project consortium in general.  Part of the work may involve travel to other partners. If the research features a prototype, it will contribute to the project’s open source library and may be supported by engineers in the team.

Deadline : 2024-01-31

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(19) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Topology Design for Decentralized Federated Learning

This PhD thesis is in the framework of Inria research initiative on Federated Learning, FedMalin https://project.inria.fr/fedmalin/. The PhD candidate will join NEO project-team https://team.inria.fr/neo/.
NEO is positioned at the intersection of Operations Research and Network Science. By using the tools of Stochastic Operations Research, the team members model situations arising in several application domains, involving networking in one way or the other.

Deadline : 2024-01-28

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(20) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Distributed Training of Heterogeneous Architectures

The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL) [LKT+20,KMA+21], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [MMR+17] allow clients to train a common global model without sharing their personal data. FL reduces data collection costs and can help to mitigate data privacy issues, making it possible to train models on large datasets that would otherwise be inaccessible. FL is currently used by many big tech companies (e.g., Google, Apple, Facebook) for learning on their users’ data, but the research community envisions also promising applications to learning across large data-silos, like hospitals that cannot share their patients’ data [RHL20]. Most existing algorithms for federated learning train the same model architecture for each user/device (personalized FL algorithms allow only the value of model parameters to be different). In this task, we will explicitly consider that heterogeneous devices may not be able to run the same model (because of computational, memory, or battery constraints) and will propose new algorithms to train heterogeneous architectures jointly. A key challenge, in this case, is to design meaningful ways of sharing information across heterogeneous model architectures.

Deadline : 2024-01-28

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(21) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Monitoring Plane for mobile cellular networks

The overall objective of the DIANA project-team is to design, implement and evaluate advanced networking architectures. To do so, the team works to provide service transparency and programmable network deployments in the context of both wired and next generation wireless cellular networks. The team’s methodology includes advanced measurement techniques, design and implementation of architectural solutions, and their validation in adequate experimental facilities. The DIANA team designed, deployed and operates R2lab, a wireless testbed designed with reproducibility as its central characteristics. The team collaborates with Eurecom to deploy and operate an open programmable platform to test post-5G services. Recently, the team enriched R2lab with 5G professional radio units and compute resources managed by Kubernetes clusters to provide an experimental cloud-native environment to test with open source (OAI, SrsLTE) software and some commercially licensed software (e.g. Amarisoft) for 5G/6G networks supporting for example scenarios with disaggregated 5G networks elements. Other recent contributions of the team include: Enhanced Transport-Layer Mechanisms for Multi-Access Edge Computing-Assisted Cellular Networks, Bencharmking Mobile Networks from the Viewpoint of Video Streaming QoE, Introducing Fidelity in Network Emulation, and Enhanced Ray Tracing Techniques for Accurate Estimation of Signal Power.

Deadline : 2023-12-31

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(22) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Experimental evaluation of sliced cellular networks

With the advent of softwarization in networks, and in next generation cellular networks in particular, the current trend is to validate network solutions over emulated testbeds that have the advantage to be flexible and easily deployed. The emulation can be done either on one physical machine like Mininet, or on a cluster of physical machines like Maxinet and Distrinet. The main challenge with network emulation is to make sure that the emulation has well passed, and was not bottlenecked by the underlying network conditions or the compute resources. Realism (or fidelity) of an emulation is a sufficient condition for reproducibility of the experiment. We aim in this thesis to propose a new framework for verifying emulation realism in the context of next generation cellular networks with the consideration of network slicing, multi-technologies and multi-actors.

Deadline : 2023-12-31

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(23) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M The role of rapport in human-conversational agent interaction: Modeling conversation to improve task performance in human-agent interaction

The doctoral student chosen for this project will acquire a broad range of skills in the analysis and synthesis of conversational behavior. S/he will build a corpus of data on the social aspects of human-human conversation within a particular domain, and annotate and analyze those data. He or she will derive computational models from the results of the data analysis as well as based on relevant prior literature, and refine those models using structural equation modeling and other tools. The doctoral student will integrate the computational models of social phenomena into a functioning ECA using machine learning approaches to dialogue systems, such as deep reinforcement learning and LSTM (among others), and will evaluate whether integrating the new models improves the performance of the ECA.  Applications of the work will be as varied as virtual personal assistants such as Alexa, Siri, and Google Now and intelligent tutoring systems.

Deadline : 2023-12-31

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(24) PhD Degree – Fully Funded

PhD position summary/title: Doctorant F/H Online matching with delays and batching

The standard mathematical formulation of the online matching problem described above is interesting but quite limited; few of the motivating examples can be appropriately modeled by it. This explains why variants already exist. Instead of generalizing the model, many “simpler” models were also considered. For instance, the celebrated establishing “prophet inequalities” consists in finding the best online matching when |U| is reduced to a singleton (or only a few numbers of them). In the latter, the additional assumption is usually that costs and/or rewards are random variables with known distribution (other variants of this problem are called secretary problems, Pandora’s boxes, etc. but the space limit prevents developing them, while still interesting). We aim at considering the impacts and/or benefits of the fact that, in practice, decisions might not be taken sequentially, at each stage, but can be batched, i.e., the decision maker can wait d time-steps before making the irrevocable decisions for those. This is highly motivated by the motivating applications and examples (as companies bundle their decisions to save time/costs, even if that is at the cost of some immediate reward). This assumption requires going beyond the worst-case examples. Indeed, given any instance of a decision process without delay, it is possible to generate another instance where the batching has no impact: simply create d − 1 dummy decisions (with 0 cost/reward).

Deadline : 2023-12-31

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(25) PhD Degree – Fully Funded

PhD position summary/title:  PhD Position F/M Contention-Aware Scheduling of Storage Resources on Exascale Systems

This thesis is placed in the context of the PEPR NumPEx (https://numpex.fr/), whose goal is to co-design the exascale software stack and prepare applications for the exascale era. This thesis will be co-supervised by Inria and CEA, respectively the Inria center at the University of Rennes and the CEA center at Bruyères-Le-Châtel, near Paris. Beyond the supervision, collaborations within the PEPR with the different laboratories of the consortium are to be expected. 

Deadline :  2023-12-16

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(26) PhD Degree – Fully Funded

PhD position summary/title:  PhD Position F/M Reliable Deep Neural Network Hardware Accelerators

The goal of this thesis is twofold: (i) designing and developing an optimized algorithm-level fault injection framework to assess the resiliency of DNN HW accelerators to HW faults, to enable the application of low- cost selective fault-tolerance strategies; (ii) designing selective fault-tolerance approaches for DNN HW accelerators by using the analysis provided by the fault injection method. The reliability improvements obtained with the above-described methodology will be measured and a design space exploration will be carried out to obtain different DNN HW accelerator implementations providing different trade-offs between fault tolerance and energy efficiency.

Deadline :2023-12-12

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(27) PhD Degree – Fully Funded

PhD position summary/title:  PhD Position F/M Hardware-guided compression & fine-tuning of Transformer-based models

The successful candidate will be a member of the TARAN team, based in the Inria Research centre at Rennes University and IRISA Lab. in Rennes, France. The thesis is part of the upcoming PEPR HOLIGRAIL project, part of the larger PEPR programme in Artificial Intelligence. It brings together researchers working on machine learning, computer arithmetic, hardware acceleration and compiler optimization for embedded systems and deep learning applications from University of Rennes, Inria, CEA List, INSA Lyon and Grenoble-INP. HOLIGRAIL is a large and competitive project that will fund more than 20 people ranging from PhD students to postdoctoral fellows.

Deadline : 2023-12-03

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(28) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Self-supervised learning for implicit shape reconstruction

Recent years have seen a surge in implicit neural shape representations for modeling 3D objects and scenes within deep learning frameworks. Thanks to their ability to continuously represent detailed shapes with arbitrary topologies in a memory-efficient way, these representations alleviate many of the shortcomings of the traditional alternatives such as polygon meshes, point clouds and voxel grids. In practice, these shape functions are typically multi-layer perceptrons mapping 3D points to occupancy or signed distance values. The zero level set of the inferred field can be rendered differentiably through variants of ray marching and tessellated into explicit meshes with Marching Cubes. Coupling these implicit neural functions with conditioning mechanisms allows generalization across multiple shapes. For instance, combining their inputs with local features generated from additional encoding networks [1,2,3,4] yields single forward pass inference models that can learn 3D reconstruction from various input modalities such as images [5,6] or partial point clouds [1,2,3,4].

Deadline :2023-11-30

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(29) PhD Degree – Fully Funded

PhD position summary/title:  PhD Position F/M PhD Position Computer Vision / Deep Learning: Video Generation

Inria, the French National Institute for computer science and applied mathematics, promotes “scientific excellence for technology transfer and society”. Graduates from the world’s top universities, Inria’s 2,700 employees rise to the challenges of digital sciences. With its open, agile model, Inria is able to explore original approaches with its partners in industry and academia and provide an efficient response to the multidisciplinary and application challenges of the digital transformation. Inria is the source of many innovations that add value and create jobs.

Deadline :  2023-11-30

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(30) PhD Degree – Fully Funded

PhD position summary/title:  PhD Position F/M Anticipation of Hand-Object Contact Configuration for Object Manipulation

Research activities in MIAI aim to cover all aspects of AI and applications of AI with a current focus on embedded and hardware architectures for AI, learning and reasoning, perception and interaction, AI & society, AI for health, AI for environment & energy, and AI for industry 4.0. This project in particular focuses on perception and robot interaction and thus will take place in close collaboration with LIG and GIPSA-Lab at the University of Grenoble.

Deadline : 2023-11-30

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(31) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Full-Body Design and Control of an Aerial Manipulator for Advance Physical Interaction

Aerial robots (commonly called “drones”) are nowadays extensively used to see the environment in applications like agriculture, mapping, etc. But, if aerial robots were also able to effectively manipulate the environment, the application domains could be further extended toward new areas like contact-based inspection, assembly and construction, and so on. The research community has previously focused on the design and control of aerial manipulators [1]. This opened the door to new applications, e.g., contact-based inspection [2]. However, current methodologies are still limited to very simple interaction tasks, involving limited contact behaviors with static and rigid surfaces (e.g., touching a flat wall with a stick attached to the robot) and in very controlled environments.

Deadline : 2023-11-21

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(32) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Aerial Robots with the sense of touch

Short Abstract: Researchers are trying to make aerial robots perform physical work. Current methodologies show promising results, but they fail in real scenarios, mostly because of inaccurate visual perception. Inspired by nature, this project investigates how to also provide aerial robots with the sense of touch and how to use it for improving their manipulation capabilities.

Deadline : 2023-11-21

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About  The National Institute for Research in Computer Science and Automation (Inria), France –Official Website

The National Institute for Research in Computer Science and Automation (Inria)  is a French national research institution focusing on computer science and applied mathematics. It was created under the name Institut de recherche en informatique et en automatique (IRIA) in 1967 at Rocquencourt near Paris, part of Plan Calcul. Its first site was the historical premises of SHAPE (central command of NATO military forces), which is still used as Inria’s main headquarters. In 1980, IRIA became INRIA. Since 2011, it has been styled Inria.

Inria is a Public Scientific and Technical Research Establishment (EPST) under the double supervision of the French Ministry of National Education, Advanced Instruction and Research and the Ministry of Economy, Finance and Industry.

 

 

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