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 The National Institute for Research in Computer Science and Automation (Inria), France.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: 2022-04675 – PhD Position F/M Markerless 3D localization of surgical tool in a per-operative context
The objective of this thesis is to develop robust methods for detection, localization and tracking of objects (without markers) in RGB-D image sequences. Using deep neural network-based approaches, we aim to detect, classify and initialize a pose computation process for surgical instruments present in the images (eg, [Rad 2017]). Then, model-based tracking and localization approaches using both contours and depth maps provided by the RGB-D camera will be proposed [Marchand, 2016, Trinh, 2018]. The complexity of the surgical instruments under consideration requires the development of GPU (Graphics Processing Unit) based approaches to ensure a fast and complete projection of the model into the images [Petit 2014]. As the camera is itself mobile, the position of the objects in a fixed reference frame (in which the anatomical landmarks will also be expressed) requires the localization of the camera w.r.t. the environment that will be done using Visual Inertial SLAM methods assisted by an IMU (Inertial Measurement Unit). Moreover, to deal with fast movements, the prediction of object position, integrating inertial data, will be managed thanks to particle filters on SE(3). To validate the system, an estimation of the measurement error will be performed by an external system giving the ground truth (either by mounting the camera on a robot or by using a Vicon 3D measurement system).
Deadline : 2022-12-31
(02) PhD Degree – Fully Funded
PhD position summary/title: 2022-05011 – PhD Position F/M Trustworthy multi-site privacy-preserving technologies
The overall goal of the TRUMPET project is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, cross-domain, cross-border European datasets with privacy guarantees that exceed the requirements of GDPR.
Deadline : 2022-12-01
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD student on federate learning and multi-party computation techniques for prostate cancer
While AI techniques are becoming ever more powerful, there is a growing concern about potential risks and abuses. As a result, there has been an increasing interest in research directions such as privacy-preserving machine learning, explainable machine learning, fairness and data protection legislation.
Privacy-preserving machine learning aims at learning (and publishing or applying) a model from data while the data is not revealed. Notions such as (local) differential privacy and its generalizations allow to bound the amount of information revealed. The goal of the multi-disciplinary FLUTE project is to advance and scale up data-driven healthcare by developing novel methods for privacy-preserving cross-border utilization of data hubs. Advanced research will be performed to push the performance envelope of secure multi-party computation in Federated Learning, including the associated AI models and secure execution environments.
Deadline : 2022-12-01
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Automated Reasoning for Set Theory
Automated deduction has made significant progress in recent years, including the development of efficient SAT and SMT solvers, and the extension of first-order deduction techniques to fragments of higher-order logic. The core objective of the thesis is to make these advancements available for system developments in the B method. Concretely, we believe that higher-order logic allows for a much more direct encoding of proof obligations expressed in a language of set theory than existing translations to first-order logic. This should be beneficial for developing specific instantiation techniques that can recognize frequent patterns that arise in B specifications, significantly raising the degree of automation.
Deadline : 2022-11-30
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(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Formalization of Set Theory and Proof Checking
The first objective of the thesis is to express TLA+ set theory and proofs in the λΠ-calculus modulo theory and implement it in Dedukti, in a way that enables interoperability with the set theory of B and Event-B. In particular, the logic of TLA+ is untyped whereas B and Event-B are based on a typed logic. It is therefore expected that it will only be possible to define partial translations between the two formalisms, exploiting the fact that many proofs do not use the full power of the theory they are expressed in. The representation of TLA+ set theory can reuse ideas from the existing encoding in the logical framework Isabelle for TLAPS, the TLA+ Proof System.
Deadline : 2022-11-30
(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Energy efficient data management: Data reduction and protection meet performance and energy
This PhD will be hosted by Inria (Myriads team, Rennes Bretagne Atlantique) and will be funded by Inria. This sub-project is a part of the Inria-OVH collaborative framework. Thus, the work will be carried out in a close collaboration with OVH. In fact, we plan to validate the results of the project using several OVH data services including backup services and media service, etc.
Deadline : 2022-11-20
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(07) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Modélisation et contrôle des systèmes à grande échelle par le processus de continuation
Les recherches actuelles en théorie du contrôle visent à maîtriser la dynamique des grands systèmes, tels que les infrastructures (trafic, transport, systèmes multi-agents, propagation des épidémies, électrification des véhicules, etc.) Face à un grand nombre de variables d’état décrivant le système, il y a un besoin des nouvelles méthodes de modélisation et control permettant le passage à la échelle.
Deadline : 2022-11-17
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Reliable and cost-efficient data placement and repair in P2P storage over immutable data
This PhD thesis will be in the context of a collaboration between HIVE and Myriads and Coast Inria teams. The Ph.D student will be located at Inria Center of the University of Rennes and will be visiting team Coast at Inria Nancy-Grand Est and the Hive offices in Cannes.
Deadline : 2022-11-16
(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD Position: Understanding Linux scheduling bottlenecks
The Linux kernel is at the core of almost all modern computing, from the smartphone to the datacenter. As such, its performance and correctness are critical. In a virtualized environment, such as the cloud, operating system functionalities are taken over by the hypervisor, which can be viewed as the new operating system. Xen and Linux-KVM are the two most popular open source hypervisors used by large scale cloud providers such as Amazon and Outscale. In order to ensure e ective and e cient support for modern applications, it is necessary to understand and address bottlenecks in both traditional operating systems and hypervisors, as well as in the interplay between them. This Phd position is part of a research project in operating systems being carried out in the {Whisper} and {WIDE} Inria teams.
Deadline : 2022-11-15
(10) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Modèle musculo-squelettique du fantassin : vers une analyse énergétique de l’activité physique en mission pour l’optimisation des équipements et du chargement
Les fantassins en mission sont soumis à de nombreuses sollicitations physiques (courses, franchissements d’obstacles, évacuation de blessés, chargement d’armes lourdes…) qui sont pour grande partie à l’origine de nombreuses pathologies musculo-squelettiques (pieds, genoux, dos, épaules…). Dans le même temps, l’équipement du fantassin n’a cessé de s’accroitre, ainsi que le poids porté en mission. L’ensemble de ces éléments nécessite une compréhension globale de l’activité physique du fantassin, pour minimiser l’impact de ces éléments sur la santé du soldat et maximiser sa capacité de mouvement. Il est alors possible d’exploiter ces analyses biomécaniques pour les intégrer comme des spécifications complémentaires des aspects fonctionnels dans le cahier des charges des équipements.
Deadline :2022-11-15
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Numerical methods and high performance simulation for 3D imaging in complex media
This PhD is part of the OptiGPR3D exploratory action led by IDEFIX and POEMS teams at Inria Saclay, whose objective is to introduce versatile and robust simulation tools that can adapt to complex materials while remaining efficient, in the perspective of making 3D electromagnetic imaging feasible and certifiable through interpretable and optimized inversion methods. With an a priori provided by a classical imaging method, could we design a network of emitters that can provide an optimal illumination of a target structure and a network of receivers that makes it possible to obtain an optimal 3D image? This question is motivated by the need to go beyond the current capabilities of non-destructive testing for buried infrastructures maintained by EDF.
Deadline : 2022-11-12
(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Explainable Artificial Intelligence for Rule-Based Logical Languages
Deadline : 2022-11-09
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(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Creative Visualization Sketching
The thesis is fully funded by Inria and the ANR project GLACIS, which brings together experts from Human-Computer Interaction (HCI), Information Visualization, and Computer Graphics. There are opportunities for collaboration with Inria Sophia Antipolis (Computer Graphics), as well as Inria Bordeaux, the École Centrale de Lyon, and the University of Toronto (Visualization and HCI). We also foresee close interactions with design experts.
Deadline :2022-11-07
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M AI for Arrhythmia Prediction
The Inria Sophia Antipolis – Méditerranée center counts 34 research teams as well as 7 support departments. The center’s staff (about 500 people including 320 Inria employees) is made up of scientists of different nationalities (250 foreigners of 50 nationalities), engineers, technicians and administrative staff. 1/3 of the staff are civil servants, the others are contractual agents. The majority of the center’s research teams are located in Sophia Antipolis and Nice in the Alpes-Maritimes. Four teams are based in Montpellier and two teams are hosted in Bologna in Italy and Athens. The Center is a founding member of Université Côte d’Azur and partner of the I-site MUSE supported by the University of Montpellier.
Deadline : 2022-11-05
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Deep-based semantic representation of avatars for virtual reality (Inria/InterDigital Ys.ai project)
Inria and InterDigital recently launched the Nemo.ai lab dedicated to research on Artificial Intelligence (AI) for the e-society. Within this collaborative framework, we recently initiated the Ys.ai project which focuses on representation formats for digital avatars and their behavior in a digital and responsive environment, and are looking for several PhDs and post-docs to work on the user representation within the future metaverse. This PhD position will focus on exploring, proposing and evaluating novel solutions to represent both body and facial animations with semantic-based approaches for the animation of avatars in a context of multi-user immersive telepresence.
Deadline : 2022-10-31
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Social and safe navigation of autonomous vehicles in erratic environment
The PhD thesis is funded by the ANR project Annapolis (https://project.inria.fr/annapolis/) and co-supervised by Anne Spalanzani (CHROMA team in Grenoble), and Philippe Martinet (ACENTAURI Team in Sophia Antipolis).
- The overall objective of CHROMA is to address fundamental and open issues that lie at the intersection of the emerging research fields called “Human Centered Robotics”, “Multi-Robot Systems” and “AI for humanity”. Their goal is to design algorithms that allow autonomous agents to perceive, decide, learn, and finally adapt to their environment. Their approach for addressing this challenge is to bring together probabilistic methods, machine learning, planning techniques, multi-agent decision making, and constrained optimisation tools. This is done in cooperation with other disciplines such as sociology for the purpose of taking into account human models, or physics to consider self-organized systems. Two main themes are addressed: i) Perception and situation awareness in human-populated environment, by focusing on bayesian perception and sensor fusion, ii) Decision making for single and multi-robot systems.
- ACENTAURI is a robotic team led by Ezio MALIS that studies and develop intelligent, autonomous and mobile robots that can help humans in their day-to-day lives at home, at work or during their displacements. The team focuses on perception, decision and control problems for multi-robot collaboration by proposing an original hybrid model-driven / data driven approach to artificial intelligence and by investigating efficient quantum algorithms. The team focuses on robotic applications in smart territories, smart cities and smart factories. In these applications several collaborating robots will help humans by using multi-sensor information eventually coming from infrastructure. The team demonstrates the effectiveness of the proposed approaches on real robotic systems like cars AGVs and UAVs together with industrial partners.
Deadline : 2022-10-31
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(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD Thesis – Optimal urban mobility network design for sustainable space sharing between vehicles and soft transport modes
Nowadays, we are witnessing a rapid spread of multimodal mobility in our cities and a willingness on the part of communities to promote new mobility behaviors. These changes are causing road networks to evolve and grow with modifications that are often far from being optimally designed, and public authorities are beginning to investigate how to integrate new paths and roads for the new soft transportation modes (bicycles, e-scooters, etc.). This is a very critical and relevant problem for cities and traffic authorities, which do not have updated nor easy-to-use tools to evaluate the impact of their network design decisions. In practice, several questions remain unanswered such as which road network structure is best suited to support new changes in its capacity, to ease space sharing, and eventually to sustain the replacement of certain roads in favor of soft-mobility dedicated lanes.
Deadline : 2022-10-31
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Deep interactive control of virtual character’s motion based on separating identity, motion and style (Inria/InterDigital Ys.ai project)
Inria and InterDigital recently launched the Nemo.ai lab dedicated to research on Artificial Intelligence (AI) for the e-society. Within this collaborative framework, we recently initiated the Ys.ai project which focuses on representation formats for digital avatars and their behavior in a digital and responsive environment, and are looking for several PhDs and post-docs to work on the user representation within the future metaverse. This PhD position will focus on exploring, proposing and evaluating novel solutions to represent both body shape and movements in a compact latent representation. This representation aims at simplifying the adaptation of the shape (identity) of a user, or/and his motion, and/or the style of both his shape and motion (such as transferring the user’s moving shape to a fictional character with different properties and style).
Deadline : 2022-10-31
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M 3-year PhD position in Computational Models of Semantic Memory and its Acquisition
We invite applications for a 3-year PhD position at the University of Lille in the context of the recently funded research project “COMANCHE” (Computational Models of Lexical Meaning and Change). The position is funded by Inria, the French national research institute in Computer Science and Applied Mathematics. The position is affiliated with the MAGNET team at Inria, Lille [1] as well as with the SCALAB group at University of Lille [2] in an effort to strenghten collaborations between these two groups, and ultimatelyfoster cross-fertilizations between Natural Language Processing and Psycholinguistics
Deadline : 2022-10-31
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Koopman operator modelling of non-linear dynamical systems for ensemble methods.
The goal of this project is to incorporate statistical learning tools in ensemble-based data assimilation methods for large scale dynamical systems from fluid mechanics and geophysical flows. Especially, it is to learn eigenfunctions of the Koopman operator restricted to a reproducing kernel Hilbert space (RKHS) transported by the dynamical system. This RKHS transported in time by the dynamical system constitutes a manifold sampled by the ensemble of trajectories, in which efficient estimations can be performed. This manifold has so nice mathematical properties, that it is given the nickname of “Wonderland”. The objective will be (1) to exploit and develop theoretical aspects based on the RKHS and the Koopman operator in order to determine mathematical features relevant for data assimilation (tangent linear, Lyapunov exponents, transport of covariance matrices, localisation by the kernel, etc.) (2) to learn dynamical systems coming form numerical simulations of oceanic flows and fluid mechanics (3) develop new data assimilation methods in Wonderland, inspired by classical methods (ensemble Kalman filter, ensemble variational methods, particle filters, etc.).
Deadline : 2022-10-31
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Fairness in Federated Learning
The selected PhD student will be mainly based in Lille in the MAGNET team but will also frequently visit the COMETE team. The main objective of the COMETE team is to develop principled approaches to privacy protection to guide the design of sanitization mechanisms in realistic scenarios. Similarly, the main objective of the MAGNET team is to develop ethically acceptable machine learning algorithms focusing on privacy, federated learning, and fairness and to empower end users of artificial intelligence.
Deadline : 2022-10-31
(22) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Codes LDPC quantiques issus de revêtements topologiques
Le candidat recruté sera amené à effectuer des recherches en mathématiques et informatique dans le domaine des codes correcteurs quantiques.
Deadline : 2022-10-31
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M 3-year PhD position in Automatic Argumentation Mining in French Legal Decisions
We invite applications for a 3-year PhD position co-funded by Inria, the French national research institute in Computer Science and Applied Mathematics, and LexisNexis France, leader of legal information in France and subsidiary of the RELX Group. The position is affiliated with the MAGNET, a research group at Inria, Lille, which has expertise in Machine Learning and Natural Language Processing, in particular Discourse Processing. The PhD student will also work in close collaboration with the R&D team at LexisNexis France, who will provide their expertise in the legal domain and the data they have collected.
Deadline : 2022-10-31
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Self-supervised learning for implicit shape reconstruction
Recent years have seen a surge in implicit neural shape representations for modeling 3D objects and scenes within deep learning frameworks. Thanks to their ability to continuously represent detailed shapes with arbitrary topologies in a memory-efficient way, these representations alleviate many of the shortcomings of the traditional alternatives such as polygon meshes, point clouds and voxel grids. In practice, these shape functions are typically multi-layer perceptrons mapping 3D points to occupancy or signed distance values. The zero level set of the inferred field can be rendered differentiably through variants of ray marching and tessellated into explicit meshes with Marching Cubes. Coupling these implicit neural functions with conditioning mechanisms allows generalization across multiple shapes. For instance, combining their inputs with local features generated from additional encoding networks [1,2,3,4] yields single forward pass inference models that can learn 3D reconstruction from various input modalities such as images [5,6] or partial point clouds [1,2,3,4].
Deadline : 2022-10-31
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M IOT-ML : Secure Machine Learning on IOT Traces for Daily Activity Discovery
The PETRUS team (Inria/UVSQ), in association with the Hippocad company (a subsidiary of La Poste group) and the Yvelines District, is currently deploying secure home boxes for 10,000 patients. These boxes, based on the team’s research results (DBMS embedded in secure hardware), include a personal medical-social database to improve care coordination for dependent people at home. Medical and social workers interact with these secure boxes via a smartphone application. Our objective is to enhance these boxes with the ability to communicate with IoT sensors measuring e.g., luminosity, movement, and temperature to improve patient monitoring. The sensors’ raw data will be analyzed by Machine Learning (ML) techniques to identify the patient’s activities and thus, detect risk situations like depression or illness. These raw data are however very intrusive. The originality of our approach is to process these raw data inside each box, within the hardware security element, in order to externalize only the relevant information: alerts, aggregated values, and patient dashboards.
Deadline : 2022-10-31
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M (BMI 2022) Design of a new parallel task-based programming model for composable numerical design
The compose project aims at designing a wide range of high-performance numerical algorithms being able to run efficiently from a basic laptop up to a modern supercomputer. While versatility is a desirable feature in software engineering in general – and is mainstream in most computer science fields –, it still remains a hard challenge in a high-performance computing (HPC) context. The reason is that optimization (a required feature in HPC) is often hard to reach together with genericity. In the last decade, task-based programming has emerged as a solid candidate for abstracting algorithms while ensuring performance portability [1,2]. It consists in representing (and encoding) a numerical algorithm as a directed acyclic graph (DAG) where vertices represent tasks and edges represent dependencies between tasks. The idea is that the actual execution can then be delegated to a third party software (often referred to as a runtime system). We have designed a full solver stack following this model, at the top of which is the maphys++ hybrid solver for solving systems of linear equations. The outcome is that the resulting software stack is both efficient and versatile in terms of target architectures. However, it still lacks versatility in terms of numerical algorithms that can be addressed by the stack.
Deadline : 2022-10-31
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Video-based dynamic garment representation and synthesis
The Ph.D. position is part of a joint laboratory between Interdigital, a leading technology and research company, and Inria, the French national institute of computer science and automation. In particular, the Ph.D. is shared between an Interdigital team in Rennes, Inria Morpheo team in Grenoble, and Inria Mimetic team in Rennes.
Deadline : 2022-10-30
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Interpretable Deep Learning for image-based species identification
With data-driven AI methods becoming prevalent in more and more aspects of our life, there is a growing effort within the AI community to ensure that these methods do not only perform well, but also communicate to humans some elements of the learned internal reasoning that leads to a particular output. This effort towards Explainable AI (XAI) [1] aims at ensuring that humans can understand the process within the AI model, ultimately allowing for a more human-centric AI. The vast majority of methods, those based on feature attribution, aim at pointing out which parts of the input are most responsible for the output, potentially resulting in an ambiguous interpretation that may end up being misleading [2, 3]. We want to explore how to achieve richer and more useful types of explanations, such as those using prototypical examples [4] or natural language [5], and explore the impact that these explanations have on the users in terms of trust and of how much the users are able to learn from the system. In addition, rich explanations, such as those in the form of visual characteristics in natural language, can be designed to provide an interpretable representation of each data sample that can be leveraged for few-shot and zero-shot learning, as long as the characteristics of new classes are known.
Deadline : 2022-10-26
(29) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Analyse statistique des données à structure de graphes
L’objectif est de développer des outils statistiques et topologiques pour la comparaison et l’analyse de distribution de données représentées par des graphes.
La personne recrutée sera amenée à mettre au point des outils mathématiques et algorithmiques pour comparer les distributions de graphes. Une analyse mathématique approfondie des garanties théoriques offertes par les outils développés sera conduite.
Deadline : 2022-10-21
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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