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: Doctorant F/H Perception et interaction via des Avatars en réalité virtuelle
The thesis aims to analyse and understand the impact of using an avatar on perception and interaction within a virtual environment derived from a digital twin. The first objective is to study how the presence of such an avatar may influence the perception of the characteristics of the virtual site, including scales, spatial properties, or ambiance. The challenge is to determine to what extent the users’ virtual embodiment in a given avatar modifies their relationship to space and the way they apprehend it.
In a second phase, the thesis will examine how the avatar can improve direct interaction with the 3D content, as well as with other users present in the same virtual environment. The objective is to understand how the avatar can make these interactions more efficient, more natural, or more expressive, and how it can contribute to a better appropriation of the digital twin.
Deadline :2026-08-31
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD – Robust few-shot learning for foundational model in CT imaging
The Greater Paris University Hospitals Data Warehouse (EDS AP-HP) contains multimodal clinical data (PMSI, imaging, biological, and clinical documents) for over 14 million patients. The ANR FM2AI projet proposes to leverage 50,000 real-world clinical 3D CT scans from this exceptional data resource, to deploy a novel foundation model for abdominal-pelvic CT Imaging. The approach is designed to generalize across multiple clinical applications involving abdominal CT images, by resorting to self-supervised learning techniques for training the
foundation model, and then exploiting it for a wide class of clinical queries thanks to the innovative
few-shot learning paradigm [1], while paying attention to robustness assessment.
In this context, we are seeking for a PhD candidate with an excellent background in AI and mathematics, to design robust few-shot learning methods to allow the on-site adaptation of the foundation model and generalization to specific diagnostic tasks, such as prediction and segmentation of CT images of all body regions, without requiring massive re-annotation efforts nor GPU resources. The work will build upon the expertise of the OPIS team on few-short learning [2,3,4,5].
Deadline : 2026-08-31
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Formal Verification of Liveness in Distributed Systems using Reinforcement Learning
We are interested in developing algorithms for automatically proving the absence of livelocks or detecting livelocks bugs in distributed protocols using directed testing algorithms. We suggest developing deep RL algorithms to analyze maximal wait times in distributed protocols. The wait time is the number of steps a process is executed before it gains access to a resource. There are no livelocks if the wait times are always finite. The work consists in modeling this problem as an RL problem, choosing the right rewards and RL algorithms, and making sure it scales to real implementations of distributed algorithms.
Here the RL agent chooses at each step the schedule, that is, which process to execute, whether there are packet losses etc. and observes the next global state of the system. It receives a reward of 1 at each step a process waiting to access a resource is executed but without accessing that resource. Thus, the distributed protocol can be seen as a game which the RL agent must learn how to play to exhibit the worst-case behavior.
The model and RL algorithms can be chosen either to attempt to prove the absence of livelocks and compute bounds on wait times, or to detect livelock bugs. The precise direction to be taken and the weight given to RL versus formal verification in this work can be chosen according to the student’s background and preferences.The work also includes an extensive bibliographic study, the development of the above algorithms, implementation and experiments.
Deadline : 2026-08-31
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD – Building a foundational model for abdominal CT imaging
The Greater Paris University Hospitals Data Warehouse (EDS AP-HP) contains multimodal clinical data (PMSI, imaging, biological, and clinical documents) for over 14 million patients. The ANR FM2AI projet proposes to leverage 50,000 real-world clinical 3D CT scans from this exceptional data resource, to deploy a novel foundation model for abdominal-pelvic CT Imaging.
In this context, we are seeking for a PhD candidate with an excellent background in AI, to build an open-source foundation model adapted to abdominal 3D CT radiology, along with a versatile toolbox to integrate it in task-oriented tools for an effective clinical integration. The model will be validated on three downstream tasks of abdominal-pelvic oncology, in coordination with our AP-HP partners, namely (1) Colonic Cancer Staging ; (2) HCC Treatment Optimization ; (3) Pancreatic Cancer Resectability and Prognosis.
Deadline : 2026-08-31
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Generalized Wasserstein barycenters and applications
The main objective of this thesis is to expand our understanding of Wasserstein barycenters, focussing in particular on the case with signed weights. In particular, we aim to produce a computationally tractable characterization of such objects and provide efficient numerical algorithms for their computation. We will also invesitigate different applications of the developed tools, including:
- the discretization of Wasserstein gradient flows, developing the ideas of [2];
- the discretization of fluid models such as the pressureless and isentropic Euler equations;
- the development of accelerated schemes for optimization on the space of measures.
[1] Thomas O. Gallouët, Andrea Natale, and Gabriele Todeschi. Metric extrapolation in the wasserstein space, 2025. Calculus of Variations and Partial Differential Equations
[2] Thomas O. Gallouët, Andrea Natale, and Gabriele Todeschi. From geodesic extrapolation to a variational BDF2 scheme for Wasserstein gradient flows. Mathematics of Computation, 93:2769–2810, 2024.
Deadline : 2026-08-30
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(06) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Analyse biomécanique in situ et reconstruction 3D holistique (athlète-matériel-environnement) pour l’optimisation de la performance en voile de compétition.
En voile de compétition, particulièrement sur des supports à foil (Kitefoil, planche à voile IQfoil), la performance repose sur un équilibre instable. La posture de l’athlète détermine directement le “couple de rappel” et l’assiette du flotteur, éléments cruciaux pour maximiser le chargement aérodynamique et la vitesse. Évaluer ces postures en situation réelle est un enjeu majeur pour optimiser le matériel et nourrir les modèles de prédiction de vitesse (VPP).
Cependant, le milieu maritime est hostile (eau, embruns, luminosité variable, vastes espaces). Les méthodes traditionnelles d’analyse biomécanique (marqueurs optoélectroniques type Vicon ou capteurs inertiels intrusifs) y sont inopérantes ou faussent la performance. Ce projet propose donc de développer une approche par vision par ordinateur sans marqueurs (markerless) à partir de vidéos embarquées ou de drones, pour analyser la cinématique du sportif dans son environnement naturel.
Deadline : 2026-08-15
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Multi-Fidelity Scientific Machine Learning with Heterogeneous Inputs
Many engineering and scientific problems involve complex physical phenomena that are difficult—and sometimes impossible—to reproduce experimentally. Moreover, experimental campaigns are often costly in time, resources, and logistics. In this context, numerical simulation plays a central role for prediction, design, and decision support.
In modern applications, models are frequently multi-physics, involve strong couplings across scales, and require high-dimensional parameterizations. A single high-fidelity simulation of the full system is often too expensive to be used repeatedly, for instance in optimization, uncertainty quantification (UQ), calibration, or control. An illustrative example is computational hemodynamics. In this application, fully resolved simulations of blood flow in patient-specific arterial geometries require solving the three-dimensional, time-dependent Navier–Stokes equations, often coupled with vessel wall elasticity and boundary conditions inferred from clinical data. While such simulations provide detailed information, they are computationally intensive, which prevents their systematic use in large parametric studies. Consequently, simplified or surrogate models (e.g., 1D network models, reduced-order models, data-driven surrogates) are widely used to obtain fast, approximate predictions.
A major scientific challenge is therefore to combine information from models of different fidelity levels in a principled manner, in order to achieve the accuracy of high-fidelity simulations while maintaining computational tractability. This is the objective of multi-fidelity modeling.
Deadline : 2026-08-01
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Dynamic Approximate Computing for Energy-Efficient AI Hardware Accelerators
The primary objective of this thesis is to investigate and advance the design of energy-efficient AI accelerators by dynamically applying approximate computing techniques and to advance hardware-software co-design methodologies.
The research will build upon recent advancements in efficient domain-specific architectures for AI. The goal is to develop novel approaches that balance performance, energy efficiency, and accuracy, while addressing the unique challenges of implementing approximate computing in real-world AI systems.
Deadline : 2026-07-31
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD Position – Structural Methods for Mixed Model/Data Digital Twin Engineering
The PhD will be conducted within Hycomes team of the Inria center at Rennes University and in the realm of the Engineering Digital Twins (EDT) program, a national initiative aiming to advance the science and engineering of digital twin systems.
The candidate will work in a research environment combining expertise in:
- modeling languages such as Modelica
- hybrid systems and dynamical systems
- structural analysis of differential-algebraic equations
- large-scale simulation and digital twin architectures
Deadline : 2026-07-31
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Filippov Solutions for Discontinuous Differential-Algebraic Equations (DAEs): Control and Simulation
Differential-algebraic equations (DAEs) arise naturally when modeling dynamical systems from first principles. In many cases, physical laws are expressed as combinations of differential and algebraic equations. This modeling approach is common in constrained mechanics, chemical and biological processes, power systems, and especially analog circuit design—where idealized components (e.g., resistors, capacitors, inductors) and Kirchhoff’s laws define the system dynamics. When these systems experience abrupt changes—such as switching in electric circuits, mechanical contacts, or discontinuous control inputs—discontinuous DAEs emerge. However, there is currently no comprehensive theoretical foundation for studying such systems. Challenges include:
- Their hybrid behaviors, which differ significantly from ODE counterparts,
- The inconsistent initialization problem caused by switching and algebraic constraints,
- The occurrence of Dirac impulses due to state jumps.
Without a rigorous solution concept, tasks such as simulation, stability analysis, and control design lack solid justification.
Deadline :2026-07-31
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Visuo-tactile control for manipulating deformable objects with robotic arms combining tactile perception and photometric visual servoing
A significant challenge in robotics is the ability to interact with deformable objects. Most existing robotic control frameworks are designed for manipulating rigid objects, limiting their applicability to complex tasks. To extend robotic manipulation capabilities, this PhD thesis aims to develop a novel control approach that leverages tactile sensors and depth (RGB-D) cameras to manipulate soft objects.
The core idea is to integrate visual and tactile feedback to track object deformations in real time and develop a visuo-tactile control strategy that enables one or more robotic manipulators to apply and regulate desired deformations. This capability is crucial for various applications, including robotic hand manipulation of soft materials, assembly and disassembly of flexible components, and precise handling of elastic materials. Within the French PEPR Robotics DRMI project, the research will focus specifically on industrial recycling, particularly the dismantling of equipment with flexible parts.
Controlling the deformation of soft materials requires understanding how robotic manipulations translate into material deformations. Existing approaches address this challenge using either:
- Data-driven methods, which estimate deformation behavior based on past visual observations [1], [12].
- Geometric-based models, such as ARAP [11] or LARAP [10].
- Physics-based models, such as Finite Element Models (FEM) [2-3] or mass-spring models [4-5].
Deadline : 2026-07-30
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD Position F/M: How does Reasoning with LLM Help Repair Vulnerabilities in Repo-level Software Projects?
Large Language Models (LLMs) have demonstrated remarkable capabilities in automating the detection of software vulnerabilities (SVD) due to their ability to process both natural and programming languages. However, a critical reliability concern with state-of-the-art LLMs is their susceptibility to adversarial attacks. Subtle, problem-space modifications to source code—such as variable renaming or dead code insertion—can mislead the model without changing the code’s main functionality or underlying vulnerabilities. Furthermore, the opaque, “black-box” nature of LLMs makes it difficult to understand whether they truly grasp code semantics or simply recognize superficial statistical artifacts.
Deadline : 2026-07-30
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Optimal Control and Experimental Design (Project OCARINA)
Project OCARINA (Optimal Control frAmework for Robust Iterative NonlineAr experimental design) is an ANR-funded research project aiming to advance experimental design through applications of optimal control theory, motivated by collaborative research on retinal experimentation with computational and experimental neuroscientists. Experimental design seeks to maximize the information obtained from experiments while minimizing cost, time, and material use. In model-based systems, this often involves reducing uncertainty in parameter estimates, which is particularly crucial
when experiments are expensive or limited in duration.
When the system dynamics are described by a dynamical model, experimental design can be naturally formulated as an optimal control problem. Controls (such as light stimuli) are chosen to optimize a criterion related to the Fisher information. This perspective enables the use of geometric and functional analysis to derive qualitative insights and guide the design of effcient experimental protocols.
The project focuses on three main challenges:
- Control-theoretic perspective: develop the geometric theory of experimental design based on optimal control, formalizing experimental design problems and analyzing qualitative properties of optimal solutions (e.g., structure of singular trajectories, Hamiltonian formulation).
- Ensemble control and uncertainty: represent unknown parameters as distributed ensembles and develop robust strategies that account for variability and incomplete knowledge applying recent advances on optimal control in in nite dimension (such as measure-valued dynamics).
- Neuroscience application: apply these methods to retinal experiments, where each preparation is time and resource-limited, so that e cient design has immediate, tangible impact.
Deadline : 2026-07-03
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Optimizing serverless computing in the edge-cloud continuum
Serverless computing, also known as function-as-a-service, improves upon cloud computing by enabling programmers to develop and scale their applications without worrying about infrastructure management [1, 2]. It involves breaking an application into small functions that can be executed and scaled automatically, offering applications high elasticity, cost efficiency, and easy deployment [3, 4].
Serverless computing is a key platform for building next-generation web services, which are typically realized by running distributed machine learning (ML) and deep learning (DL) applications. Indeed, 50% of AWS customers are now using serverless computing [5]. Significant efforts have focused on deploying and optimizing ML applications on homogeneous clouds by enabling fast storage services to share data between stages [6], by solving the cold-start problem (launching an appropriate container to perform a given function) when scaling resources [7], and by proposing lightweight runtimes to efficiently execute serverless workflows on GPUs [8]; and on building simulation to evaluate resource allocation and task scheduling policies [9] . However, few efforts have focused on deploying serverless computing in the Edge-Cloud Continuum, where resources are heterogeneous and have limited compute and storage capacity [10], or have addressed the simultaneous deployment of multiple applications.
Deadline : 2026-06-30
(15) PhD Degree – 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.
The PhD student will be supervised by:
- Shadi Ibrahim, MAGELLAN team in Rennes
- Cedric Tedeschi, MAGELLAN team in Rennes
Deadline : 2026-06-30
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Use of short distances for gradient descent in seismic imaging via full-waveform inversion
As part of the MathSout targeted project under the PEPR Maths-VivES initiative, the goal is to investigate the use of weak metrics inspired by optimal transport to improve the performance of gradient descent algorithms used in seismic imaging via full-waveform inversion.
Regular trips to Grenoble to collaborate with Ludovic Métivier (ISTerre) are planned. These will be covered within the limits of the current reimbursement schedule.
Deadline : 2026-06-30
(17) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Modélisation statistique et apprentissage automatique pour le suivi de la biodiversité végétale
Le projet européen BEAGLE (“Biodiversity methods for advanced monitoring at large scales”) vise à transformer le suivi de la biodiversité à grande échelle grâce à l’intégration de données issues de capteurs, de plateformes participatives, de télédétection et d’ADN environnemental.
Dans ce cadre, Inria et Pl@ntNet recrutent un·e doctorant·e pour développer de nouvelles méthodes de modélisation statistique et d’apprentissage automatique appliquées au suivi de la biodiversité végétale et des habitats.
La thèse sera réalisée au sein d’Inria à Montpellier, en interaction étroite avec Pl@ntNet, plateforme de référence pour l’analyse automatisée de données de biodiversité végétale et la science participative. Le ou la doctorant·e évoluera dans un environnement interdisciplinaire associant sciences des données, écologie, télédétection et bioinformatique, dans le cadre d’un consortium européen rassemblant des acteurs majeurs du monitoring de la biodiversité.
Les travaux s’appuieront sur des jeux de données massifs combinant observations participatives, données environnementales, imagerie et données géospatiales.
Deadline : 2026-06-30
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M University of Toulouse – The levers of sobriety and conviviality and their influence on algorithmic decision models
This thesis is part of the ANR SOCLOUD project, which aims to explore the possibilities of designing a sustainable and open data center that respects planetary boundaries and is socially equitable toward its users. The goal is to go beyond mere energy efficiency concerns, whose gains are almost systematically offset by first-order or higher-order indirect negative effects, such as the rebound effect.
The thesis will be co-supervised between IRIT in Toulouse and FEMTO-ST in Besançon, with the recruited person working at the IRIT site in Toulouse. The SEPIA team at IRIT works on resource management in distributed systems and has extensive experience in energy-saving and sufficiency issues. The DEODIS team (FEMTO-ST) focuses on the design, optimization, and evaluation of distributed and intelligent systems. It is recognized for its work on energy-constrained scheduling and renewable energy management.
The recruited person will be supervised by Patricia Stolf, Professor at the University of Toulouse, Georges Da Costa, Professor at the University of Toulouse and Veronika Sonigo, associate professor at Université Marie et Louis Pasteur in Besançon. Funding will be provided through the SOCLOUD project, involving both academic and industrial partners. The PhD candidate will be integrated into a national collaborative environment with regular project meetings. The gross monthly salary will be approximately 2050 euros.
Deadline : 2026-06-30
(19) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Abstractions composables pour les méthodes multigrilles : de la conception numérique à l’exécution parallèle
La résolution efficace de grands systèmes linéaires creux constitue un enjeu central du calcul scientifique et de la simulation industrielle. Les méthodes multigrilles figurent parmi les approches les plus performantes, offrant une complexité optimale ou quasi optimale pour de nombreuses classes de problèmes.
Au cours des dernières décennies, des bibliothèques majeures comme Trilinos et PETSc ont introduit des formes avancées de modularité et de composabilité, permettant notamment de combiner différents lisseurs, opérateurs de transfert ou solveurs grossiers. Toutefois, ces approches restent en pratique fortement contraintes par des couplages entre algorithmes, structures de données et modèles d’exécution, ce qui limite la flexibilité et complique l’exploration de nouvelles variantes.
Cette thèse propose de pousser plus loin la composabilité, en repensant les méthodes multigrilles comme des assemblages de briques numériques indépendantes. L’objectif n’est pas de remplacer les approches existantes, mais de s’inscrire dans leur continuité en explorant la possibilité d’une couche d’abstraction plus légère, plus explicite et plus flexible.
Deadline : 2026-06-30
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Post-Quantum Protocol Transition for Internet-of-Things Systems
Deadline : 2026-06-30
(21) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Model-Checking Linear Dynamical Systems under Floating-Point Rounding
Développer des algorithmes pour la vérification partielle (invariants) ou complète de systèmes dynamiques linéaires sous arrondis.
Un sujet proche donnant plus de détails peut être trouvé ici : https://elefauch.github.io/Project/floating_point.pdf
Le travail sera réalisé en collaboration avec Joël Ouaknine et David Purser. Des visites à Liverpool et Sarrebruck sont donc à prévoir au cours de la thèse.
Deadline : 2026-06-28
(22) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Vérification formelle de messagerie sécurisée post-quantique
Dans le cadre du projet PEPR PQ TLS (https://pepr-pq-tls.cnrs.fr/objectives/), l’objectif est de participer au dévellopement de preuve formelle de messageire sécurisé en utilisant l’assistant de preuve Squirrel, un projet dynamique dont le développement se déroule sur les sites de Rennes , Paris et Nancy.
Deadline : 2026-06-22
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Ontologies for research processes
Description of experimental and simulation settings is key to interpretation and reproducibility of scientific results. However, they are not currently described in a way that would make them exploitable automatically. We aim to define representations of scientific processes enabling their query, analysis, comparison and reproduction.
Research reliability relies partly on data recording and communication. Although data is important, it is not less important to record and publish the processes that led to the production of this data. These data may be collected through confirmatory experiments, simulations or evaluations. In order to be useful, process descriptions must refer to many facets of the process such as hypotheses, code and model, parameters, measure collected.
Recording such processes in a relatively formal way brings many opportunities:
- Reproducibility: automatic process rerun and data re-analysis
- Repurposability: production of new processes by modifying the description [Werner et. al., 2024];
- Presentation: automatic generation of process reports;
- Collection: aggregating experiment descriptions for retrieving, querying and comparing them [Euzenat, 2022]. Ideally, it will be possible to generate a meta-analysis on a specific topic from a set of descriptions.
This contributes to the objective to make research data Findable, Interoperable, Accessible and Reproducible, i.e. FAIR [Wilkinson et. al., 2016].
We aim at developing formal descriptions of research processes that enable this. The goal of this thesis proposal is to design, develop and evaluate descriptions expressed with relevant `semantic’ technologies.
Deadline : 2026-06-22
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Distributed Training of Machine Learning Models with Malicious Clients
This PhD thesis is part of the Inria–Hivenet Challenge Cupseli: Collaborative Unified Platform for a Scalable and Efficient Learning Infrastructure. Cupseli aims to enable large-scale AI training and inference on distributed, heterogeneous, and potentially volatile computing resources, while preserving security, privacy, and performance.
The PhD candidate will be hired by Hivenet (soon to become Antimatter) in Cannes, but mostly hosted at the Inria Centre at Université Côte d’Azur, in Sophia Antipolis, and will work in the Inria team NEO, as well as with Hivenet. The thesis is part of the Security and Privacy axis of Cupseli, which focuses on protecting distributed learning systems against malicious behavior and information leakage.
Deadline : 2026-06-21
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Frugal Distributed Training with Volatile Resources
This PhD thesis is part of the Inria–Hivenet Challenge Cupseli: Collaborative Unified Platform for a Scalable and Efficient Learning Infrastructure. Hivenet, which will soon become Antimatter, aims to develop scalable, efficient, and secure solutions for running AI training and inference workloads on distributed, heterogeneous, and volatile computing resources.
The PhD candidate will be hired by Hivenet and hosted mostly by the NEO project-team at the Inria Centre at Université Côte d’Azur, in Sophia Antipolis. The thesis will be carried out in close collaboration with the Inria ARGO project-team and with Hivenet engineers. The work will focus on the design of new distributed training algorithms tailored to environments where participants share computing and storage resources only for limited periods of time.
Deadline : 2026-06-21
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Concurrent Autonomic Control for the Computing Continuum
This thesis aims to improve the MAPE–K loop by investigating the overlap between the Plan and Execute phases, possibly running several P–E cycles in parallel, under explicit consistency and conflict-resolution guarantees. More precisely, the objectives of the thesis is to design, formalize, and evaluate a concurrent coordination pattern for MAPE–K, referred to as MACPE–K, for service orchestration in the Cloud-Edge-IoT continuum. The work will formally establish the conditions under which the Plan and Execute phases can overlap while guaranteeing consistency and resolving action-level conflicts, and then model the end-to-end responsiveness/cost trade-off, including the Monitoring and Analysis phases. The dynamic tuning of the degree of concurrency and of the triggering policies will be investigated in order to preserve responsiveness under varying workloads and environmental conditions. Service placement will serve as the primary case study, but a second self-management task (e.g., elastic autoscaling or fault recovery) will also be instantiated to substantiate the genericity of the pattern, covering both stateless and stateful services
Deadline : 2026-06-21
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M When Our Avatars Transform Us: Enhancing the VR User Experience by Modifying Avatar Movement
The main aim of this project is to explore the perception of people’s body movements (of self and others) when they are applied to a virtual character in virtual reality (VR) and to develop motion modifiers which will change the content of the motion without diminishing its naturalness. The first step will be to develop a motion filter in VR (using animation blueprints in Unreal Engine 5) which will be applied to a virtual character, designed for a user to embody and control it with their own body movements (Xsens motion capture). Through a series of experiments with participants, embodied in VR, we will investigate the perceptual effects of the created motion filter. We expect that understanding the perception of motion modification of such avatars could help us create enriched experiences for users of VR applications in health and education, to enhance the outcomes of therapy/training [1], as well as to imagine new forms of interaction and communication (e.g., VRChat).
Deadline : 2026-06-20
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Physically-based skin models for 3D reconstruction and temporal tracking from multi-video observations
This PhD will explore new encodings of the SDF-grid based on a combination of factored tensors [A,B] and efficient factored representations of the radiance function, rooted in analytical Gaussian lobe decompositions of the radiance function as for [C,D]. The former have been used to model opacity fields in the context of volumetric NeRF, but seldom applied to SDF-based rendering approaches. We believe the latter can be significantly simplified to the point of eliminating most neural components to focus on physically accurate, interpretable radiance parameterizations, in a way that can be combined with spatially efficient encodings. Recent work [D] shows a promising lead to obtain high precision reconstructions with more explainable and complete surface radiance models. But their approach does not easily scale to real-world complex human data, with a very high compute overhead. Most interestingly, simplified but expressive models of skin subscattering have been proposed on the basis of dipole (2-lobe) angular Gaussian parametrizations [30], allowing to explicitly model different Fitzpatrick skin indices [F]. We thus postulate that a natural and unified Gaussian-lobe parametrization of light interaction exists and would simultaneously lead to a sparse, lightweight, relightable and differentiable representation of the scene that could complement current surface estimation algorithms, and ultimately drastically improve their performance with natural scenes containing humans. Our intuition is also that such a proposition would easily lend itself to scalable implementations able to reach millimetric detail with enhanced reconstruction performance due to better radiance model expressivity. The scalability can be achieved by using recent factored models projecting coordinates of a 3D query point on lower dimensional spaces such as planes [A,G,H], which have been recently generalized to a more versatile framework [B] with multiscale capabilities and yet very simple implementations. This multiscale capability is particularly interesting to encode hierarchical feature sets based on sparse Gaussian lobe sets that could be combined over various spatial levels in the hierarchy for improved expressivity. Various novel research contributions will be proposed and explored on the basis of such an encoding to optimize pipeline decoding, ray-batching, ray-marching, appearance and color decoding benefiting from this new targeted combination of models.
Deadline : 2026-06-20
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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