Inria, France invites online Application for number of Fully Funded PhD Positions 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 Positions – Fully Funded
PhD position summary/title: PhD Position F/M SPORTSVIZ: Advanced Situated Visualizations for Sports Videos
- The PhD student will enroll at the Universite-Paris Saclay (12th worldwide in the Shanghai ranking in 2024 and the top ranking French university) in the computer science graduate school . The student will be hosted in the Aviz team at Inria, which is the French national research institute dedicated to digital science and technology.
- Location: Bât 660, Digiteo Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette
- The PhD funding is available for a duration of three years. While the previous page lists September as the starting date, the money for the project is available from now and therefore earlier start dates are possible.
Deadline : 2026-03-17
(02) PhD Positions- Fully Funded
PhD position summary/title: PhD Position F/M Cost and Performance-Efficient Caching for Massively Distributed Systems
This PhD will be in the context of IPCEI-CIS (Important Project of Common European Interest – Next Generation Cloud Infrastructure and Services) DXP (Data Exchange Platform) project involving Amadeus and three Inria research teams (COAST, CEDAR and MAGELLAN). This project aims to design and develop an open-source management solution for a federated and distributed data exchange platform (DXP), operating in an open, scalable, and massively distributed environment (cloud-edge continuum).
The PhD student will be recruited and hosted at the Inria Center at Rennes University; and the work will be carried out within the MAGELLAN team in collaboration with other partners.
Deadline : 2026-02-28
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(03) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M Optimizing serverless computing in the edge-cloud continuum
This PhD position is part of the PEPR Cloud – Taranis project funded by the French government (France 2030). The position will be recruited and hosted at the Inria Center at Rennes University; and the work will be carried out within the MAGELLAN team in close collaboration with the DiverSE team and other partners in the Taranis project.
The PhD student will be supervised by:
- Shadi Ibrahim, MAGELLAN team in Rennes
- Olivier Barais, DiverSE team in Rennes
- Jalil Boukhobza, ENSTA, Brest
Deadline : 2026-02-28
(04) PhD Positions – Fully Funded
PhD position summary/title: Doctorant F/H Anisotropic Mesh Adaptation for Aerothermal Simulations
Numerical simulation has been booming over the last thirty years, thanks to increasingly powerful numerical methods, computer-aided design (CAD), the mesh generation for complex 3D geometries, and the coming of supercomputers (HPC). The discipline is now mature and has become an integral part of design in science and engineering applications. This new status has led scientists and engineers to consider numerical simulation of problems with ever increasing geometrical and physical complexities. A simple observation of this chart
CAD –> Mesh –> Solver –> Visualization / Analysis
shows: no mesh = no simulation along with “bad” mesh = wrong simulation. We have concluded that the mesh is at the core of the classical computational pipeline and a key component to significant improvements. Therefore, the requirements on meshing methods are an ever increasing need, with increased difficulty, to produce high quality meshes to enable reliable solution output predictions in an automated manner.
Mesh adaptation is an innovative method for controlling errors in numerical simulations by generating meshes that are adapted to the geometry and physics of the problem being studied. It results in a powerful methodology that reduces significantly the size of the mesh required to reach the desired accuracy. Thus, it impacts favorably the simulation CPU time and memory requirement. Moreover, as the generated adapted mesh is in agreement with the physics of the flow, for some applications, this is the only way to obtain an accurate prediction. In fact, mesh adaptation enables a full control of discretization errors on the geometric model and the solution. Thus, it is a first step in the certification of numerical solutions by the obtention of mesh converged solutions, i.e., providing high-fidelity numerical simulations.
Nowadays, mesh adaptation is a mature tool which is well-posed mathematically. And, as it is fully automatic, it has started to be used in {industrial R&D departments} (Safran, Airbus, MBDA, Boeing, NASA,…). Indeed, it has already proved, throughout many publications and applications, its superiority with respect to fixed mesh. However, its domain of application is in majority restricted to inviscid steady or unsteady flows. It has been recently extended to turbulent flows for steady problems.
The goal of this thesis is to develop this breakthrough numerical technology for aerothermal simulations in the turbomachinery community. This thesis will be done in collaboration with Safran Tech.
Deadline : 2026-01-31
(05) PhD Positions – Fully Funded
PhD position summary/title: Doctorant F/H Doctorant F/H PhD Position : Bias, Fairness, and Fidelity in Image and Video Generation Methods
Recent progress in generative AI has revolutionized the creation and manipulation of visual media. Models such as Stable Diffusion, DALL·E, and Sora have demonstrated the ability to generate highly realistic images and videos from textual descriptions. These models are increasingly applied to editing tasks — such as virtual try-on, style transfer, and image restoration — where maintaining both semantic coherence and visual fidelity is crucial
However, the deployment of these systems also raises serious ethical and technical concerns. Research has shown that generative models can encode and amplify societal biases present in their training data, leading to unfair performance across demographic groups (e.g., gender, race, body type, or age). In editing scenarios, this may manifest as disproportionate errors, inconsistent realism, or stereotypical representations for certain groups. Furthermore, maintaining fidelity — i.e., ensuring the edited output remains consistent with the original input outside modified regions — remains a key challenge. Diffusion models, by design, regenerate entire images from noise, often unintentionally altering unedited regions and compromising visual integrity. Balancing fairness and fidelity within a stochastic generative process is thus both a scientific and ethical frontier for AI research.
This PhD will systematically investigate bias, fairness, and fidelity in diffusion-based image and video generation models, particularly within editing tasks. It will develop new frameworks for evaluating, understanding, and mitigating bias while preserving high fidelity in generative outcomes.
Deadline : 2026-01-17
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(06) PhD Positions- Fully Funded
PhD position summary/title: Doctorant F/H CIFRE – Apprentissage par renforcement pour l’optimisation de l’allocation de ressources dans un contexte de communications IoT par satellite
Ce projet est une CIFRE hébergée par le Centre de Compétences Hardware & Logiciel embarqué de Thales SIX GTS France, en collaboration avec Inria Lyon. Le poste sera co-localisé entre Thales (2/3) et Inria (1/3).
Dans le contexte des réseaux de communications pour l’internet des objets (IoT) par satellite, on recherche des méthodes d’accès multiples performantes et leur paramétrage dynamique permettant d’optimiser les capacités du système pour la liaison montante vers un ou plusieurs satellites à orbite basse (low earth orbit – LEO).
L’ objectif est de développer des solutions à base de réseaux de neurones profonds utilisant en particulier l’apprentissage par renforcement (RL). On recherchera des solutions algorithmiques et les architectures associées prenant en compte les contraintes du système, à savoir : un feedback limité, les capacités de décodage de la couche PHY pour gérer les collisions selon le récepteur mutli-utilisateur considéré. On s’intéressera à des solutions de type single-agent RL (SARL) quand l’allocation est centralisée ou multi-agent RL (MARL) quand la décision est distribuée et laissée aux nœuds.
Deadline : 2026-01-17
(07) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M Privacy-Preserving Collaborative Learning of Large Language Models Across Heterogeneous Learning Paradigms
The widespread deployment of Large Language Models (LLMs) has given rise to diverse adaptation paradigms that accommodate varying computational and infrastructural constraints. In addition to traditional full fine-tuning, emerging methods such as prompt-tuning, parameter-efficient tuning like LoRA (Low-Rank Adaptation), and in-context learning for model adaptation with reduced computational resources or without access to model weights. These approaches open up new possibilities for collaborative learning in privacy-sensitive contexts, where multiple clients aim to improve LLM performance without exposing their raw data.
This PhD thesis will focus on designing privacy-preserving collaborative learning strategies for LLMs, starting in a homogeneous setting, where all participants rely on the same adaptation paradigm. This initial step will build a foundation for tackling the more ambitious and impactful goal of heterogeneous collaboration, where clients operate under different adaptation regimes due to diverse privacy, computational, or architectural constraints. A central challenge of the project is to reconcile contributions from such heterogeneous clients in a unified learning process, while ensuring rigorous privacy guarantees—most notably through differential privacy (DP), which provides strong theoretical protections against data leakage. The thesis will also address the trade-offs between model utility and privacy risk and propose novel mechanisms specifically tailored to this multi-paradigm collaborative learning scenario.
The PhD candidate will be based at Inria Lille, within the MAGNET research team, and will be co-supervised by M. Tommasi, Dr. Raouf Kerkouche (Inria Lille) and Dr. Cédric Gouy-Pailler (CEA Saclay). The research will benefit from a stimulating scientific environment, combining Inria’s strong expertise in machine learning and artificial intelligence with the applied research focus of the CEA. This thesis is part of the REDEEM project, funded by the PEPR IA initiative (France 2030). It offers a highly interdisciplinary environment bridging machine learning, natural language processing, and privacy-enhancing technologies, with opportunities for national and international collaboration.
Deadline :2026-01-15
(08) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M Improving post-stroke motor rehabilitation through EMG and gamification (F/M)
Strokes are the leading cause of disability in France. Motor rehabilitation is crucial to reducing the effects of strokes. Although rehabilitation with a physiotherapist is essential, their availability is limited. This is why telerehabilitation is an important complement to post-stroke care. The challenges of telerehabilitation include the difficulty for patients to verify that exercises are being performed correctly and to maintain the motivation to follow their rehabilitation programme regularly.
This thesis is a collaboration between the Loki research team, specialising in Human-Machine Interaction (HMI), and Myodev, a company developing a rehabilitation solution based on EMG sensors. Our goal is to improve EMG telerehabilitation by accurately measuring muscle activity to ensure that exercises are performed correctly. In addition, we want to integrate gamification elements to encourage patients to follow their rehabilitation programme regularly.
Deadline : 2026-01-14
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(09) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M High order boundary conforming adaptive meshing, and boundary conditions (AIRBUS/INRIA)
The proposed PhD program is a CIFRE program between Airbus and Inria. The student will be employed by Airbus and academically supervised by Inria researchers and Airbus engineers. They will spend half of their time at Inria (Bordeaux) and half at Airbus (Toulouse).
Safer and cheaper aircrafts require new concepts, and increasingly complex geometrical, physical, and numerical modeling. These new models must integrate multi-physical interactions between aerodynamics, propulsion, structures and materials. The systematic verification and validation of these models allows to integrate CFD in the certification process, reducing our reliance on costly experimental testing. It is with this goal in mind that AIRBUS, DLR, and ONERA have launched a new common CFD software project, called CODA.
This new CFD platform, designed for efficient parallel and heterogeneous architectures, permits the integration of advanced interoperable CFD components, including in particular Finite Volume (FV) as well as Finite Element (FE) methods, namely Discontinuous Galerkin (DG) schemes. For both FV and DG approaches, CODA provides an extension towards high-orders of accuracy in the form of the consideration of approximating polynomial degrees greater than 1 for DG and high-order k-exact formulations for FV. Both classes of schemes have demonstrated a strong potential for aerodynamic problems of applied interest. Contrary to low order FV schemes commonly used in the industry, DG and k-exact FV schemes provide high-order of accuracy on unstructured meshes, furthermore, high-order approximations offer a flexible framework in which the spatial resolution can be conveniently adapted, not only by local mesh modification (h-adaptive), but also by locally varying the degree p of the polynomial reconstruction. Local p-adaptation can reduce dissipation and dispersion errors in regions where the solution is smooth thus allowing for the accurate resolution of flow phenomena with a lower number of degrees of freedom (DOFs) as compared to standard FV methods. Regarding DG methods, thanks to their compactness, they can also easily handle complex geometries and irregular meshes with hanging nodes, which simplifies the implementation of h-adaptive techniques.
An important issue in aircraft design concerns the accuracy and efficiency of predictive numerical tools for cruise conditions. The potential of adaptive high order methods in providing error reduction, as well as automated error control and related CPU time savings in flow simulations is nowadays well established. There is however an essential element which must absolutely be taken into account to tap into this potential : the proper treatment of boundary conditions. This involves several independent aspects. The first is the availability of an appropriate high order geometrical representation of the boundaries. In this project this is somehow expected to be available from the design process under the form of appropriate CAD descriptions, with underlying spline approximation. A second necessary aspect is the ability to produce a discrete approximation of such geometry with accuracy compatible with the high order approximation used to discretize the flow equations. The last one is a numerical approximation of the boundary condition itself. This approximation must be equally compatible with the error levels of the discretization scheme.
The availability of a high quality curved mesh is a necessity to obtain the desired accuracy, and is still one of the bottlenecks to allow the adoption of higher order techniques as operational tools in an industrial environment. For realistic applications involving complex curved 3D geometries (e.g. ONERA M6 wing, of full wing-bod models as the CRM or XRF1), especially in the transonic regime, the impact the geometrical accuracy on the correct prediction of shock structures and boundary layer separation may be enormous. A lot of progress has been made in recent years on different techniques allowing to obtain with an acceptable level of automation good quality curved meshes. These techniques involve either curving straight faced meshes, or the use of some optimization or variational approach. An important aspect is the ability of combining the above techniques with metric based mesh adaptation techniques built from error indicators extracted directly from the flow solver. Concerning the approximation of the boundary condition itself, when dealing with finite elements, the most classical approach is to work with an iso-parametric approximation in which the geometry as well as the flow solution are approximated by some high order polynomial.
Initial work on metric based hp adaptation techniques within CODA for DG discretizations on unstructured meshes has been successfully applied to a range of flow configurations and models, including 2D and 3D detached turbulent and laminar flows using Navier-Stokes, RANS and ZDES model equations.
Deadline : 2026-01-07
(10) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M Defining and implementing metrics for Cloud sufficiency
Since the beginning of the 21st century, the importance of digital technology in general and data centers (DC) in particular has only grown with exponential expansion in recent years. Initially invisible, DCs represent a significant share of global electricity consumption, which could rise from 2% today to 10% in 2030. Even if the efficiency of each DC increases, this does not compensate for their proliferation which reached 67 million servers in 2022 according to ADEME. The historical models proposed for digital consumption by Anders et al. in 2015 propose several scenarios. As early as 2018, the worst-case scenario was already observed in the field. Thus, DC consumption in Ireland exceeded that of urban households, representing 20% of the country’s production, which jeopardizes decarbonization projects in other sectors of the economy. This issue is entering societal debates and is increasingly being covered by the media in articles or reports.
Deadline :2026-01-07
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(11) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M Defining and implementing metrics for Cloud sufficiency
Since the beginning of the 21st century, the importance of digital technology in general and data centers (DC) in particular has only grown with exponential expansion in recent years. Initially invisible, DCs represent a significant share of global electricity consumption, which could rise from 2% today to 10% in 2030. Even if the efficiency of each DC increases, this does not compensate for their proliferation which reached 67 million servers in 2022 according to ADEME. The historical models proposed for digital consumption by Anders et al. in 2015 propose several scenarios. As early as 2018, the worst-case scenario was already observed in the field. Thus, DC consumption in Ireland exceeded that of urban households, representing 20% of the country’s production, which jeopardizes decarbonization projects in other sectors of the economy. This issue is entering societal debates and is increasingly being covered by the media in articles or reports.
Deadline : 2026-01-07
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(12) PhD Positions – Fully Funded
PhD position summary/title: Doctorant F/H Deciphering long-range communications within macromolecular complexes
This 3-year PhD position is funded by the prestigious Programme Inria Quadrant (PIQ) for the project DynaNova, which aims to advance our understanding of conformational dynamics and allosteric communication in macromolecular complexes. The successful candidate will develop novel co-operative message-passing graph transformer architecture that learns conformational heterogeneity from molecular dynamics (MD) simulations by encoding the underlying dynamics of atomic interactions and correlations in macromolecular complexes. You will join the Delta team at Inria (Université de Lorraine), working closely with Dr. Yasaman Karami and Dr. Hamed Khakzad, experts in conformational dynamics, allostery, and deep learning for structural biology. The team is growing and offers a highly interdisciplinary environment that brings together researchers in structural bioinformatics, computational chemistry, biophysics, and machine learning. We have access to major national HPC facilities (Grid5000, Jean Zay, GENCI allocations), including large-scale GPU resources.
Deadline :2026-01-05
(13) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M PhD in Integrated and Printed Circuit Design for Sustainable and Ubiquitous Electronics
We are looking for an ambitious 3-year PhD candidate to join a European project, GAIA, studying the subject of biodegradable ultra low-power electronics. The position will start at the commencement of the project, 1-Feb 2026.
The GAIA project aims to design electronics for a ubiquitous and ecologically-aware Internet of Things. The project will solve this with a completely revolutionary approach to electronic design by using transient and biodegradeable materials. These new devices will be able to perform computation and communication with energy harvested from the environment and from ambient wireless signals. The PhD student will be part of the design and electronic characterization of these devices as well as proof-of-concept prototype electronics that will be manufactured in more traditional solid-state processes.
As part of this project, you will be expected to work closely with a multi-national European consortium consisting of experts in telecommunications, embedded systems, RFID, integrated circuits, and printed electronics. You will also be supported closely, day-to-day, with a researcher, an engineer, and a postdoctoral scholar.
Deadline : 2026-01-05
(14) PhD Positions – Fully Funded
PhD position summary/title: Doctorant F/H Modélisation de transitions non-linéaires en écoulement magnétohydrodynamique.
Le sujet portera sur la modélisation numérique d’instabilités magnétohydrodynamiques à l’origine du magnétisme dans les étoiles de type solaire. Modéliser l’amplification des champs magnétiques par instabilité dynamo est en effet un problème crucial pour comprendre l’évolution des étoiles, notamment leur vitesse de rotation. Il s’agit également d’un élément clé pour retracer l’histoire du Soleil. Ce problème est d’autant plus actuel que les prédictions théoriques disponibles sont largement incompatibles avec les données d’observation récemment obtenues grâce à la mission spatiale Kepler (Nasa). Un aspect important du projet portera également sur la caractérisation des conséquences observables, et plus particulièrement de la signature des champs magnétiques sur les signaux sismiques, pour des étoiles analogues au Soleil.
Deadline : 2026-01-03
(15) PhD Positions – Fully Funded
PhD position summary/title: pre-PhD student – Action Detection for improving autism diagnosis
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 can explore original approaches with its partners in industry and academia and provide an efficient response to the multidisciplinary and application challenges of digital transformation. Inria is the source of many innovations that add value and create jobs.
Deadline : 2026-01-03
(16) PhD Positions – Fully Funded
PhD position summary/title: Doctorant F/H Doctorant F/H PhD Position : Control, Motion Fidelity, and Computational Efficiency in Long-Form Audio-Visual Video Generation
Recent advances in generative AI have dramatically expanded the ability to synthesize and manipulate video content. Large-scale diffusion transformers and autoregressive video models — such as Sora — now exhibit impressive capabilities in generating high-resolution, multi-second clips from textual prompts. These systems increasingly support multimodal conditioning (text, images, audio), showing early signs of temporally consistent storytelling and complex scene dynamics.
Despite this progress, several fundamental challenges remain unsolved. First, audio-visual controllability remains limited: while models can loosely synchronize audio and video, they struggle with precise alignment of speech, actions, and environmental events. Second, current systems lack fine-grained motion control, making it difficult to specify nuanced trajectories, subtle character actions, or physically plausible object interactions. Third, the generation of long-duration videos (over tens of seconds or minutes) introduces severe problems of temporal drift, memory accumulation, semantic inconsistency, and scene fragmentation. Finally, the computational demands of high-resolution, long-context generative models pose serious barriers to both training and deployment. Scaling video models in space-time while maintaining quality is currently prohibitively expensive and technically challenging.
This PhD will investigate the foundations of controllability, motion fidelity, temporal consistency, and computational efficiency in audio-visual video generation. It will develop new frameworks and methodologies that allow generative models to produce globally coherent, fine-controlled, long-range audio-visual sequences, while significantly reducing computational overhead. These contributions aim to advance the scientific understanding of generative video modeling and address core barriers impeding real-world applications in film production, simulation, robotics, and AR/VR systems.
Deadline :2025-12-27
(17) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M LLM-Powered Continuous Evolution of Scientific Computing Software
The candidate will be involved in the DiverSE team, joint to the CNRS (IRISA) and Inria, and in the Laboratory in High Performance Computing for Calculation and Simulation (LiHPC) of CEA DAM, affiliated to the University of Paris-Saclay. It will be supervised by Benoit Combemale ( https://people.irisa.fr/Benoit.Combemale/) and Djamel Khelladi ( http://people.irisa.fr/Djamel-Eddine.Khelladi/) from Inria, and Dorian Leroy from CEA DAM. The candidate can be either at Inria in Rennes or CEA DAM in Bruyère le chatel, and visit regularly the other site.
Deadline : 2025-12-19
(18) PhD Positions – Fully Funded
PhD position summary/title: PhD Position F/M PhD student for Weakly-supervised video anomaly detection
This Project aims to detect critical situations in the CCTV video stream. Weakly-supervised video anomaly detection (wVAD) has recently gained popularity thanks to its ability to provide frame-level binary labels (i.e., 0: Normal, 1: Anomaly) using only video-level labels during training. Despite decent progress on simple anomaly detection (such as an explosion), recently proliferated methods still suffer from complex real-world anomalies (such as shoplifting). This is mainly due to two reasons: (I) undermining the anomaly diversity during training: previous methods assemble diverse categories of anomalies under a unified label, thereby ignoring the category-specific key attribution. (II) Lack of precise temporal information (i.e., weak-supervision): limits the ability of the methods to capture complex abnormal attributes that can viably blend with normal events. Towards addressing this, we plan to first decompose the anomaly diversity into multiple experts for encoding category-specific representations and then to entangle pertinent cues of each expert by exploiting the semantic intercorrelation between them. Further, existing anomaly detection methods primarily focus on immediate detection, lacking the capability to anticipate anomalies well in advance. This shortcoming is particularly critical in systems where early warning can prevent anomalies. By leveraging the strengths of auto-regressive models, which predict future values based on historical data, we aim to extend the predictive horizon, allowing for timely and informed decision-making.
Deadline :2025-12-19
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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