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 Signal processing-based on the squared eigenfunctions of the Schrodinger operator. Application to EEG signals
Biomedical signals are usually characterized by the presence of peaks that provide direct or indirect information on the physiological and metabolic state. Usually, these pulse-shaped signals require preprocessing such as denoising and artifact removals. Further analysis and processing might be required in some specific situations to allow for the extraction of pertinent information from these signals. Despite the fact that the literature abounds with signal processing methods, there are still challenges that need to be overcome and further improvements can be achieved for processing pulse-shaped signals. In this context, a quantum-based signal processing method has been proposed in 1. This method called the semi‐classical signal analysis (SCSA) method decomposes the signal into a set of functions given by the squared eigenfunctions of the Schrödinger operator associated with its negative eigenvalues 1. Thus, and unlike traditional signal decomposition tools, the SCSA expresses the signal through a set of functions that are signal dependent, that is, these functions are not fixed and known in advance but are computed by solving the spectral problem of the Schrödinger operator whose potential is the signal to be analyzed. Accordingly, these eigenfunctions capture more details about the signal and its morphological variations 2. The SCSA has been successfully applied in many applications for signal representation, denoising, post‐processing, and feature extraction. For example, it has been used for arterial blood pressure waveform analysis in 3,4 and for magnetic resonance spectroscopy (MRS) denoising 5, for MRS water suppression 6, and for MRS lipid suppression 7. It has been also used for feature extraction in epileptic seizure detection 8 and for the characterization of PPG signals and blood pressure signals for non-invasive estimation of central pressure and arterial stiffness respectively 9. Recent work on the characterization of EEG signals for epileptic seizure detection and seizure onset zone location has shown promising results 10, 11.
Deadline : 2026-09-30
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Modeling and Simulation of Exascale Storage Systems
This thesis is placed in the context of NumPEx (https://numpex.fr/), a key national project whose goal is to co-design the software stack for the exascale era and prepare applications accordingly. 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 NumPEx with the different partners of the consortium are to be expected.
Deadline : 2024-12-31
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Deep Neural Network-assisted computational design of highly efficient ultrafast dynamical metasurfaces
The present doctoral project is part of a collaborative project between the Atlantis project-team from the Inria Research Center at Université Côte d’Azur and the CNRS-CRHEA laboratory in Sophia Antipolis, France.
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 Research Center for Heteroepitaxy and its Applications (CRHEA) is a CNRS research laboratory. The laboratory is structured around the growth of materials by epitaxy, which is at the heart of its activities. These materials are grouped today around the theme of high bandgap semiconductors: gallium nitrides (GaN, InN, AlN and alloys), zinc oxide (ZnO) and silicon carbide (SiC). Graphene, a zero bandgap material, epitaxially grown on SiC, completes this list. Different growth methods are used to synthesize these materials: molecular beam epitaxy (under ultrahigh vacuum) and various vapor phase epitaxies. Structural, optical and electrical analysis activities have been organized around this expertise in epitaxy. The regional technology platform (CRHEATEC) makes it possible to manufacture devices. In terms of applications, the laboratory covers both the field of electronics (High Electron Mobility Transistors, Schottky diodes, tunnel diodes, spintronics, etc.) and that of optoelectronics (light-emitting diodes, lasers, detectors, materials for nonlinear optics, microcavity structures for optical sources, etc.). The laboratory has also embarked on the “nano” path, including both fundamental aspects (nanoscience) and more applied aspects (nanotechnology for electronics or optics).
Deadline : 2024-12-31
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD student on privacy-preserving federate learning with applications in oncology
This PhD student 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. Statistical privacy allows for bounding 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.
Deadline : 2024-12-31
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M 15 PhDs on Tensor Modelling, Geometry and Optimisation
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.
Deadline : 2024-12-31
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(06) 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.
The concept of Physics-Informed Neural Networks (PINNs) introduced in [1], and later revisited in [2], is one typical example. PINNs are neural networks trained to solve supervised learning tasks while respecting some given physical laws, described by a (possibly nonlinear) PDE system. PINNs can be seen as a continuous approximation of the solution to the PDE. They seamlessly integrate information from both data and PDEs by embedding the PDEs into the loss function of a neural network. Automatic differentiation is then used to actually differentiate the network and compute the loss function.
Deadline : 2024-11-30
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Decentralized semantic data sharing with access control
Data exchange and sharing is a common need in virtually all modern applications. To achieve interoperability among different, heterogeneous databases, graph databases, and in particular knowledge graphs, are preferred due to their flexibility, which enables them to describe different structures of the underlying databases. Knowledge graphs are also naturally suited to be enriched by ontologies, which describe the known concepts and properties that hold in a given application domain. Ontology-Based Data Access (OBDA) is the name commonly given to data integration systems based on knowledge graphs and ontologies; they have been successfully deployed in a variety of applications.
Our team is a parter in DXP (Data Exchange Platform), a collaborative project between several Inria teams and Amadeus, technology provided for the travel industry. Within DXP, we will work to develop scalable, decentralized, and secure OBDA mechanisms for exchanging data across the different partners involved in a travel application: providers of services such as transport and lodging, travel operators, individual travelers, etc.
To work on this topic within the DXP project, we are seeking a PhD student with a strong background in computer science, logic, and data management.
Deadline : 2024-09-30
(08) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Description automatisée de scènes audio explicable et frugale
Inria Défense&Sécurité (Inria D&S) a été créé en 2020 pour fédérer les actions d’Inria répondant aux besoins numériques des forces armées et forces de l’intérieur. La thèse sera réalisée au sein de l’équipe de recherche en traitement de l’audio de Inria D&S, sous la direction de Jean-François Bonastre et co-encadrée par Raphaël Duroselle.
La description automatisée de scènes audio consiste à présenter aux opérateurs un condensé des informations présentes dans la scène en question, sous la forme d’un texte augmenté. Ce condensé permet de faire ressortir de façon synthétique et visuelle les informations les plus importantes, tout en structurant efficacement l’accès aux informations précises. Pour illustrer ce point, un condensé pourrait prendre la forme suivante : « Dans cet enregistrement d’une durée de cinq minutes, trois locuteurs différents sont présents. Le locuteur A correspond à une identité connue dans la base de données et s’exprime en Français avec un fort accent du Monawa, les locuteurs B et C sont inconnus dans la base de données et s’expriment en Français dans leurs interactions avec A et dans une langue non identifiée lorsqu’ils parlent ensemble. Les voix de B et C présentent de fortes similitudes avec les locuteurs de la région du Quabar oriental. Le thème général de l’enregistrement concerne un transfert de marchandises entre les villes de Orienta et de Flagrance. La date du 8 Juillet 2023 est citée à trois reprises ». En cliquant sur A, l’opérateur disposera des informations sur A et sur les détails de l’identification vocale réalisée. L’accès aux segments temporels pendant lesquels A a parlé et à la transcription de ceux-ci sera direct. Dans cette transcription, les noms de personnes, de lieux ou les dates (les entités nommées) seront mises en évidence.
Deadline : 2024-09-01
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD student F/M Detection and clustering of spoken language
Inria Defense & Security (Inria D&S) was created in 2020 to bring together Inria’s actions meeting the digital needs of the armed forces and internal forces. The thesis will be carried out within the audio processing research team at Inria D&S, under the direction of Jean-François Bonastre and co-supervised by Raphaël Duroselle.
The thesis is part of a project aimed at explainable and frugal voice profiling. Voice profiling consists of extracting information from an audio recording such as identity, language spoken, age, geographic and ethnic origin, or even socio/patho/physiological marks in the voice. The objective of this project is to provide explainability to voice profiling systems without loss of performance. Explainability keeps operators at the center of the process, giving them the means to make an informed decision.
Deadline : 2024-09-01
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Explainable and frugal audio scene description
The automatic audio scene description task is to present operators with a summary of the information present in the scene, in the form of augmented text. This text provides a visual summary of the most important information, while efficiently structuring access to specific information. Here is an illustrative example of a summary: « This five-minute recording features three different speakers. Speaker A corresponds to a known identity in the database and speaks French with a strong Monawa accent, speakers B and C are unknown in the database and speak English in their interactions with A and use an unidentified language when talking to each other. The voices of B and C show strong similarities with speakers from the Eastern Quabar region. The main theme of the recording concerns a transfer of goods between the cities of Orienta and Flagrance. The date July 8, 2023 is mentioned three times.». Clicking on A gives the operator information about A and details of the voice identification performed. There will be direct access to the time segments during which A spoke and to their transcription. The transcription will highlight names of people, places or dates (named entities).
Deadline : 2024-08-31
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Scalable Translation Validation for High-Performance Computing and Machine Learning
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.
Deadline : 2024-08-31
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Computer-Assisted Collaborative Design of Transit Maps
The Inria Saclay 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 40 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.
Deadline : 2024-08-31
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Misinformation trajectories: detecting and tracing disinformation across heterogeneous data sources
The reports from the fact-checker have confirmed a lot of effort is wasted in re-reviewing previously fact-checked claims. Often different linguistics transformations like paraphrasing, introducing sarcasm, and etc are used for reformulating the same claim after some time gap. In this work, the student will work on identifying, modeling, and detecting these transformations from linguistic as well as computational perspectives. We intend to develop a framework that uses NLP techniques to identify the same claims resurfacing over and over again and further, model and compute the provenance of the claims capturing information about the transformations the claim has gone through. To work on this topic, we seek PhD candidates with strong backgrounds in computer science, NLP, and data management.
The PhD student will be part of the vibrant CEDAR team and will conduct her research in the extremely collaborative research environment of the team; she will be supervised by Prof. Ioana Manolescu ( senior INRIA researcher, part-time professor at Ecole Polytechnique), Prof. Oana Balalau (Inria researcher and part-time assistant professor at Ecole Polytechnique) and Garima Gaur (Postdoc in CEDAR)
Deadline : 2024-08-31
(14) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Doctorant(e) – Régularité de Castelnuovo Mumford des codes en métrique rang
Le centre de recherche Inria de Saclay a été créé en 2008. Sa dynamique s’inscrit dans le développement du plateau de Saclay, en partenariat étroit d’une part avec le pôle de l’Université Paris-Saclay et d’autre part avec le pôle de l’Institut Polytechnique de Paris. Afin de construire une politique de site ambitieuse, le centre Inria de Saclay a signé en 2021 des accords stratégiques avec ces deux partenaires territoriaux privilégiés.
Le centre compte , dont 27 sont communes avec l’Université Paris-Saclay ou l’Institut Polytechnique de Paris. Son action mobilise plus de 600 personnes , scientifiques et personnels d’appui à la recherche et à l’innovation, issues de 54 nationalités.
Deadline : 2024-08-31
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD student F/M Bandit theory for personalized patient monitoring.
The BIP-UP project funded by the ANR begins on January 1, 2023 and will last 4 years. BIP-UP is based on a collaboration that has existed for several years between the Inria Scool team, led by Philippe Preux, and the INSERM 1190 unit located at Lille University Hospital, led by François Pattou, both professors at the University of Lille. .
This collaboration aims to study the use of health data for personalized patient monitoring after surgical intervention. The hope is that the health data collected by different actors will make it possible to move from an identical monitoring protocol for all patients to a personalized protocol which, by being adapted to each patient, would be better followed by patients, therefore with a better benefit for their health.
Started in 2019, this collaboration has already borne fruit. On the one hand, these 3 years of work have allowed us to better identify and characterize the problems to be addressed and the ways to address them. Furthermore, in addition to permanent staff in the two teams, an engineer, a doctoral student and a post-doc have carried out significant preliminary work.
A particularly exciting aspect of this work context is the anchoring in the field since the INSERM team is in direct contact with patients (consultation, operation, follow-up) and is of course expert on the medical aspects linked to the pathology operated on.
Deadline :2024-08-01
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Artificial intelligence tools for clinical data warehouses in neuroimaging (H/F)
You will work within the ARAMIS Lab (www.aramislab.fr) at the Paris Brain Institute (https://institutducerveau-icm.org/en), one of the world top research institutes for neurosciences. The institute is ideally located at the heart of the Pitié-Salpêtrière hospital, downtown Paris. The ARAMIS Lab, which is also part of Inria (the French National Institute for Research in Digital Science and Technology), is dedicated to the development of new computational approaches for the analysis of large neuroimaging and clinical data sets. With about 40 people, the lab has a multidisciplinary composition, bringing together researchers in machine learning and statistics and medical doctors (neurologists, neuroradiologists). You will interact locally with the PhD students and engineers of the lab, as well as our medical collaborators at the Pitié-Salpêtrière hospital.
The PhD thesis will be co-directed by Ninon Burgos (Research Scientist, HDR) and Olivier Colliot (Research Director). The position is funded through the GALAN project, a large-scale national grant in collaboration between the ARAMIS Lab, the Lille Neurosciences and Cognition Research Team, the departments of neuroradiology of the Pitié-Salpêtrière hospital and of the CHU of Lille, and the teams in charge of the CDWs of AP-HP and CHU of Lille. You will be involved in these collaborations and interact with the different partners.
Deadline : 2024-07-31
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD Thesis on RL-based Decision-Making and Planning for Automated Driving
The PhD thesis focuses on decision-making and planning for automated driving, using reinforcement learning (RL). It explores how autonomous vehicles make decisions (strategic, tactical, and operational) and plan their actions while considering safety, comfort constraints, and interactions with other road users.
Decision-making systems must generate collision-free trajectories in dynamic environments while anticipating the movements of other road users. Despite advancements, challenges persist, including improving motion prediction, completeness of decision-making approaches, and enhancing system robustness against environmental data uncertainty.
The use of reinforcement learning (RL) offers promising opportunities to enhance driving policies, trajectory planning, and decision-making processes. Recent studies have demonstrated the effectiveness of RL, particularly in safe autonomous driving, multi-agent traffic management, and real-world deployment scenarios.
Deadline : 2024-07-17
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M No-brain-shift and Comprehensive Neurosurgical Navigation using computer vision
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.
Deadline : 2024-07-01
(19) 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.
The objectives of the project are to design (1) a fast electromagnetic simulation approach based on a model reduction technique, to characterize numerically the light trapping in digital imagers exploiting nanostructured pixels and, (2) a multi-objective optimization strategy of the geometrical characteristics of the nanostructuring in order to simultaneously maximize the light absorption in a pixel and to minimize the crosstalk phenomenon between neighboring pixels. For the first time, in addition to the rigorous methods for solving Maxwell’s equations, we will be able to benefit from an alternative simulation approach based on model reduction. This new approach can be used in an optimization process and the expected gain in total computation time would be between 10 to 1000.
Deadline : 2024-06-30
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(20) 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.
Reliable and efficient transmission, data privacy and security are great challenges in data sharing. Specially for 5G advanced and 6G networks data is distributed with the wide deployment of various connected Internet of Thing (IoT) devices, and are generated from many distributed network nodes, e.g., end users, small Base Stations or Distributed Units and the network edge. Also, how to col- lect/share efficiently data from multiple sources (e.g., sensors or device) up to AI/ML-based Network applications/services of Orchestration and Automation Layer (network management system) in Edge? The models shall be trained, updated regularly and operate in real-time.
Deadline : 2024-06-30
(21) 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 objective of this project is to build embodied conversational agents (also known as ECAs, or virtual humans, or chatbots, or multimodal dialogue systems) that have the ability to engage their users in both social and task talk, where the social talk serves to improve task performance. In order to achieve this objective, we model human-human conversation, and integrate the models into ECAs, and then evaluate their performance.
This project is located at the prestigious computer science institute INRIA in downtown Paris. It takes place in the context of the PRAIRIE Institute for Interdisciplinary Research on AI – one of the four 3IA institutes launched by the French government in 2019. For more information see
Deadline :2024-06-30
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Optimizing multi-domain E2E services orchestration
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.
Deadline : 2024-06-30
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Bandits-Inspired Reinforcement Learning to Explore Large Stochastic Environments
Odalric-Ambrym Maillard is a researcher at Inria. He has worked for over a decade on advancing the theoretical foundations of reinforcement learning, using a combination of tools from statistics, optimization and control, in order to build more efficient algorithms able to better estimate uncertainty, exploit structures, or adapt to some non-stationary context. He was the PI of the ANR-JCJC project BADASS (BAnDits Against non-Stationarity and Structure) until Oct. 2021. He is also leading the Inria Action Exploratoire SR4SG (Sequential Recommendation for Sustainable Gardening) and is involved in a series of other projects, from more applied to more theoretical ones all related to the grandchallenge of reinforcement learning that is to make it applicable in real-life situations.
The student will be hosted at Inria, in the Scool team. Scool (Sequential COntinual and Online Learning) is an Inria team-project. It was created on November 1st, 2020 as the follow-up of the team SequeL. In a nutshell, the research topic of Scool is the study of the sequential decision making problem under uncertainty. Most of our activities are related to either bandit problems, or reinforcement learning problems. Through collaborations, we are working on their application in various fields including health, agriculture and ecology, sustainable development. For more information, please visit https://team.inria.fr/scool/projects/
Deadline : 2024-06-30
(24) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Anatomical and microstructure informed tractography for connectivity evaluation
Diffusion MRI (dMRI) quantifies the diffusion of water molecules (constrained by their environment), enabling us to infer a number of microstructure parameters, such as the arrangement of nerve fibers, the different tissues making them up and their properties (axon diameter, proportion of neuronal cell bodies, etc.). Additionally, dMRI combined with white matter tractography techniques is a highly promising method for assessing the trajectories of nerve fibers in the brain. More specifically, tractography (illustrated in figure 1) utilizes the directionality of diffusion of water molecules in brain tissue to estimate neuronal fiber orientation [D. Jones, 2010]. This process is known as fiber tracking or fiber tractography, and the resulting collection of white matter pathways is referred to as tractograms [Mori and Van Zijl, 2002]. These approaches have the remarkable capability to delineate white matter fiber pathways, offering unprecedented insights into the structural connections within the human brain. They hold enormous potential for studying brain anatomy, development, and function [Jeurissen et al., 2019a]. Furthermore, tractography has demonstrated its substantial worth in the field of neurosurgery, playing a pivotal role in surgical planning, particularly in the preservation of critical white matter pathways during brain resections [Mancini et al., 2019].
Despite advancements in dMRI acquisition and tracking methods, white matter fiber tractography continues to grapple with certain limitations. Recent studies reported the existence of a significant number of connections that remain undetected by tractography, resulting in false negatives [D. B. Aydogan et al., 2018]. This issue poses a critical challenge, particularly in applications like surgical planning. Furthermore, the outcomes of other studies indicate that state-of-the-art tractography algorithms produce substantial numbers of false positives as well [K. Maier-Hein et al., 2017b]. This drawback hampers the accurate exploration of network properties within the brain’s connectome [Zalesky et al., 2016].
Despite this, such tractography approaches remain limited for a variety of reasons. Firstly, most of the studies use a simple diffusion model such as diffusion tensor imaging (DTI) , which cannot estimate fibers with different orientation in one voxel in complex areas. Moreover, the interpretation of changes in the measured diffusion tensor is complex and should be performed with care. Furthermore, the estimation of cerebral fibers (tractography [8], illustrated in figure 1) is not yet reliable. Studies have shown that the most advanced tractography algorithms tend to generate a large number of fiber bundles, resulting in a high false-positive rate. In this thesis, our aim was to propose innovative methods for improving fiber estimation.
Deadline : 2024-06-30
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD : Energy efficient slicing for 6G networks using AI/ML technics
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.
Deadline : 2024-06-30
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Energy-efficient QoE-aware Beyond 5G Future Mobile Networks
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.
Deadline : 2024-06-30
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Ph.D. W/M: Robust approaches in sequential decision and learning problems.
Multi-armed bandit theory has witnessed tremendous progress over the last decade, yielding algorithms achieving
strong learning guarantees (regret minimization, best-arm identification) in increasingly challenging context involving sequential decision-making in uncertain environment. In particular, recent works obtained non-parametric
optimal algorithms, enabling application of multi-armed bandit to a large range of applications when reward distributions are not easily modelled with classical families.
Recently, a complementary bandit model considering Huber-outlier distributions (mixture between an parametric distribution of interest and an arbitrary one) has been studied, offering an interesting complementary perspective compared to non-parametric assumptions and modelling arbitrarily bad outlier observations, something often encountered in real applications.
This Ph.D. will focus on the problem of model misspecification in the context, i.e. we study the problem of sequential learning when some context, or feature, vector is available but we do not want to force strong assumptions on the context and assume only nonparametric or even corrupted model on the context. To summarize, we want to study both Regret minimization or Best-arm identification objectives, in the contextual setup assuming either Huber-outlier or Non-parametric distributions.
Deadline : 2024-06-30
(28) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Traitement statistique de « low data » issus de capteurs passifs : application au suivi d’ouvrages d’art
Dans le cadre d’un projet financé par BpiFrance, cette thèse s’inscrit dans une collaboration entre l’équipe Inria MODAL, le Cerema (Centre d’études et d’expertise sur les risques, l’environnement, la mobilité et l’aménagement) et la société SilMach (société en micromécanique MEMS).
Ces dernières années ont vu une accélération de la dégradation de nos chaussées et nos ponts en raison du vieillissement des structures, de l’évolution climatique et de l’augmentation de la charge autorisée des poids lourds. Afin de mieux mesurer ces dégradations et anticiper la maintenance des infrastructures routières, plusieurs initiatives se mettent en place.
A travers leur projet commun ROAD-AI[1], Inria et le Cerema étudient conjointement les outils numériques permettant de modéliser ces phénomènes grâce à une instrumentation des structures. Cette initiative est complétée et renforcée par le projet SIRCAPASS coordonné par la société SilMach et qui vise à utiliser une nouvelle technologie de capteurs passifs MEMS (Micro Electro-Mechanical Systems) pour cette instrumentation.
Ces capteurs passifs on l’avantage d’être résilients dans des conditions hostiles tout en ne nécessitant aucun apport énergétique (« zéro énergie »). Il s’agit typiquement de composants mécaniques passifs de deux types : soit des roues dentées dont le signal de sortie correspond à un comptage fourni par les crans d’une roue en rotation, soit des systèmes de fusible mécanique dont le signal de sortie correspond à une information binaire correspondant à la casse d’un fusible (ou binaire multivarié en cas d’un train de fusibles multi-calibrés). Ces capteurs sont actionnés par des mouvements de structures (ouvrage d’art typiquement) ne nécessitant ainsi aucun branchement électrique. Leur relative « simplicité » leur permet aussi de réaliser leur temps de mission avec très peu d’entretien ou de surveillance.
Deadline : 2024-06-30
(29) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Vehicle-and-mobile phone computing sharing as part of the edge-to-cloud continuum
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.
Deadline : 2024-06-30
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Data capture and collection by energy-free sensors and ultra-low power transmission in hostile environments
In recent years, the deterioration of our pavements and bridges has accelerated as a result of ageing structures, climate change and the increase in the authorised load of heavy goods vehicles. A number of initiatives are being put in place to better measure this deterioration and anticipate the need for road infrastructure maintenance.
Through their joint ROAD-AI project, Inria and Cerema are jointly studying digital tools for modelling these phenomena using structural instrumentation. This initiative is complemented and reinforced by the SIRCAPASS project coordinated by SilMach, which aims to use a new passive sensor technology for this instrumentation.
It is within the framework of these projects that the subject of this thesis falls, the aim of which is to design wireless communication protocols that will enable efficient data collection with minimal energy consumption.
In order to put all the assets in place around this complex problem, the thesis will be hosted by Inria (at the Lille or Sophia centre) and monitored by CEREMA for the business and operational aspects. The PhD student will also be required to interact regularly with the other partners of the project, in particular SilMach, who is in charge of hardware development.
Deadline : 2024-06-30
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Mathematical models for retinal physiology and pathology [welcome package]
The PhD project focuses on the study of mathematical models for physiology and pathology of the retina, with a specific interest in degenerative diseases such as age-related macular degeneration. The overall objective is to develop new mathematical models and techniques, to deepen interdisciplinary knowledge, and to improve simulation algorithms for medical research. The general objective will be approached from different perspectives depending on the specific problem at hand, combining techniques from mathematical analysis, partial differential equations, numerical analysis, scientific computing, data analysis and artificial intelligence.
The project is intrinsically interdisciplinary and interconnected with Laboratoire Jacques-Louis Lions (UMR 7598), Centre Inria de Paris (équipe MAMBA-MUSCLEES), Hôpital National des Quinze-Vingts (Paris Eye Imaging), Institut de la Vision, Sorbonne Université and CNRS. The rich research environment offers frequent talks and visits by esteemed researchers, favouring opportunities for collaboration with leading groups in Europe and globally.
Deadline : 2024-06-30
(32) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Solution de grands systèmes linéaires creux par des méthodes de décomposition de domaine à précision mixte
La résolution de systèmes linéaires creux est l’un des problèmes fondamentaux du calcul scientifique. Malgré la puissance de calcul des supercalculateurs modernes, résoudre des systèmes de très grande taille (de l’ordre d’un milliard d’inconnues) reste hors de portée des méthodes classiques. Les méthodes de décomposition en domaines (DD) permettent de traiter de telles grandes tailles en divisant le problème global en problèmes locaux plus petits qui sont chacun résolus indépendamment et en parallèle. Une phase globale assure la continuité de la solution. Cependant, les méthodes de DD nécessitent divers noyaux d’algèbre linéaire (préconditionneurs locaux, problèmes de valeurs propres) qui sont précis pour assurer une convergence rapide, mais qui restent peu coûteux à construire, pour maintenir la capacité à passer à l’échelle sur de très grands problèmes. La résolution à la fois frugale et fiable de grands systèmes linéaires creux constitue donc l’un des défis majeurs actuels du domaine.
Deadline : 2024-06-30
(33) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Neural Linear Solvers and Preconditioners for General Sparse Matrices
Numerical simulation is a strategic tool crucial for research in several scientific fields, from Computational Fluid Dynamics (CFD) for aeronautics design to Darcy flow in porous media for CO2 storage and geothermal energy. The performance of numerical simulators is a critical factor directly impacting both result quality and the ability to perform large-scale computations. Adapting industrial codes to fully harness the capabilities of modern supercomputers (including computational accelerators such as GPUs) is a major challenge.
Deadline : 2024-06-30
(34) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Limitation de la taille des structures de données indexant les séquences génomiques.
Cette thèse s’inscrit dans le PEPR “AgroEcologie Numérique”. Elle sera encadrée par l’équipe GenScale. Le travail se fera de concert avec les membres du PEPR, en particulier du “flagship AgroDiv”.
Pour faire face aux contraintes du changement climatique, tout en répondant aux objectifs de l’agroécologie, ce groupe, principalement composé de biologistes et de bio-analyses, a pour ambition de caractériser efficacement la diversité génétique inexploitée, stockée et disponible dans les collections. Il s’agit de 20476 espèces animales (lapin, abeille, truite, poulet, porc, chèvre, mouton, bovins…) et de 7466 espèces végétales (blé, maïs, tournesol melon, choux, navet, abricotier, pois, fèverole, luzerne, tomate aubergines, pommier, cerisier, pêcher, vigne…) majeures de l’Agriculture Française.
Ce poste s’inscrit dans l’un des axes de recherche de ce groupe, consistant à développer des moteurs de recherche conviviaux pour filtrer rapidement et efficacement les données des collections et des essais sur le terrain afin d’évaluer « fonctionnellement » les accessions ou les populations d’intérêt.
Deadline : 2024-06-30
(35) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Algebraic structures in dependent types theory
The PhD thesis witll be co-supervised by Assia Mahboubi (GALLINETTE) and Cyril Cohen (STAMP/CASH). It takes place in the frame of the FRESCO ERC project, conducted by Assia Mahboubi.
Deadline : 2024-06-25
(36) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Decentralised Market-based Application Orchestration in Fog and IoT Environments
In the context of the TARANIS project (PEPR Cloud), we are offering a PhD position to investigate flexible and decentralised application orchestration in fog environments.
The work will be carried out within the MAGELLAN team (Inria Centre at Rennes University, IRISA) at Rennes. Rennes is the capital city of Brittany, situated in the western part of France. Well-connected to Paris via a high-speed train line, Rennes is a lively city and a major center for higher education and research. The work will involve close collaboration with the STACK team (IMT Atlantique, Inria, LS2N) at Nantes.
Deadline : 2024-06-23
(37) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M First Class Optimisations: Code Transformations as Libraries with Partial Evaluation and Analytic Macros
The PhD will take place in the CASH team, in LIP, Lyon, France. It will be supervised by Gabriel
Radanne, Inria researcher, specialist in compilation of high level languages. It will be made in collaboration with
Richard Membarth, Professor at DFKI-Saarbrücken, Germany.
Deadline : 2024-06-23
(38) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M hybrid aircraft energy management planning
INRIA Nancy and Airbus AI Research in Toulouse are seeking one PhD student to work on the automated control of hybrid aircraft energy management. The thesis aims to generate short-term and long-term control policies, with dynamics, states and actions that are both continuous and discrete, by hybridizing model-based and data-driven approaches.
Deadline : 2024-06-16
(39) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Sensors-based Distributed Control of Multi-Drone Systems for Agile Cooperative Aerial Manipulation
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 : 2024-06-15
(40) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Trustworthy AI hardware architectures
Nowadays, there is a growing and irreversible need to distribute Artificial Intelligence (AI) applications from the cloud to edge devices, where computation is largely or completely performed on distributed Internet of Things (IoT) devices. This trend aims to address issues related to data privacy, bandwidth limitations, power consumption reduction and low latency requirements, especially for real-time, mission- and safety-critical applications (e.g., in autonomous driving, support for gesture and medical diagnosis, smart power grid or preventive maintenance).
The direct consequence is the intense activity in designing custom and embedded Artificial Intelligence HardWare architectures (AI-HW) to support energy-intensive data movement, speed of computation, and large memory resources that AI requires to achieve its full potential. Moreover, explaining AI decisions, referred to as eXplainable AI (XAI), is highly desirable in order to increase the trust and transparency in AI, safely use AI in the context of critical applications, and further expand AI application areas. Nowadays, XAI has become an area of intense interest.
Deadline : 2024-06-11
(41) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Private and Byzantine-Robust Federated Learning
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.
Deadline : 2024-06-09
(42) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Commande partagée par cartographie musculaire pour l’assistance du membre supérieur pour des pathologies neuromusculaires et neurodégénératives
MusMapS exploite des cartographies d’efforts de sujets pathologiques pour personnaliser l’assistance d’un exosquelette du membre supérieur par commande partagée. Le couplage musculosquelettique – commande partagée pour des pathologies type neuromusculaires (myopathies) ou neurodégénératives (sclérose en plaques, post-AVC, …) est un enjeu de recherche à fort potentiel applicatif pour ces patients.
Cette action exploratoire est menée conjointement par les équipes Inria MimeTIC et Rainbow, en collaboration avec le Pôle Saint Hélier et la chaire Innovations Handicap Autonomie et Accessibilité (IH2A) de l’INSA de Rennes.
La commande partagée est maintenant devenue une référence dans le développement des systèmes d’assistance (cobots, exosquelettes) permettant de suppléer partiellement ou complètement ou d’assister l’activité d’un utilisateur. La commande partagée fait référence à une approche collaborative et coopérative dans laquelle l’homme et la machine contribuent au contrôle d’un système. Cela permet de trouver un équilibre entre l’intuition humaine et la précision de la machine. En particulier, la robotique pour le handicap s’intéresse depuis longtemps à ces questions qui nécessitent à la fois une bonne interprétation des intentions de l’utilisateur et la génération d’une commande adaptée au besoin de la tâche identifiée et de l’utilisateur. Dans le cadre de ce projet, nous nous intéresserons aux systèmes d’assistance du membre supérieur. De nombreuses stratégies de commande partagée se basent sur une commande en admittance [4]. Les applications courantes dans le cadre du handicap sont l’assistance aux tâches du quotidien, comme saisir un objet, ouvrir une porte, boire… Les patients atteints de pathologies de type neuromusculaires (myopathies) ou neurodégénératives (Parkinson, sclérose en plaques, faiblesses musculaires liées à l’âge, …) présentent une diminution de leurs capacités de générations efforts, mais aussi potentiellement de leur contrôle moteur. Cette variabilité individuelle nécessite une personnalisation du système d’assistance en fonction du niveau de déficience, à travers plusieurs “modes” de fonctionnement.
Deadline : 2024-06-08
(43) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Eco-design of parallel deformable manipulators using plant-based materials
With the current environmental crisis, there is a necessity to reduce the ecological impact of mechatronic systems, such as robotic manipulators. Indeed, these manipulators consist mostly of articulated arms composed of metallic rigid segments. The fabrication of these segments requires the extraction of metallic ore from the earth, energy to refine it in exploitable alloys, the emission of greenhouse gasses during their transport, and additional resources to shape the robot link. Fabricating the entirety of part of the links with wood reduces significantly the environmental footprint, as demonstrated in [1], [2]. However, this reduction is limited by the need to use enough wood material and energy to shape it and obtain the desired link rigidity, plant materials being intrinsically flexible. Moreover, the mechanical joints at the articulations still need to use metallic materials. In the Structuring Action 1 of the PEPR O2R project, instead of compensating for the plant material compliance, we propose to exploit it in the design of manipulators with soft and continuum robotics methodologies. The use of materials the least transformed possible, coupled with the absence of joints, will certainly lead to a strong decrease of the environmental footprint. In addition, we propose to investigate the use of parallel continuum structures, composed of several flexible legs controlling an end-effector platform, to reach a level of performance in terms of accuracy and payload for example compatible with applications like co-manipulating a load with an operator
In addition to contributing to answering a big challenge of our society today, and pursuing groundbreaking research in soft robot design by participating in a French Flagship project (PEPR O2R), the PhD candidate will have the opportunity to work with two research teams leaders in robot eco-design and soft robotics, the Armen Team, LS2N in Nantes and the Defrost Team, Inria, in Lille. They will also interact closely with an anthropologist working on evaluating the environmental footprint of robotic manipulators. The PhD student will mainly be based in Lille with several stays in Nantes, with a brut salary of 120k for the 3 years. They will work under the direct supervision of Dr. Sebastien Briot (CNRS, LS2N) and Quentin Peyron (Inria).
Deadline : 2024-06-08
(44) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Enabling Scientific Workflow Composition in Large-Scale Distributed Infrastructures
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 : 2024-06-04
(45) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Workflow Provenance and Its Application to Explainable and Transparent Artificial Intelligence
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 : 2024-06-03
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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