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44 PhD Degree-Fully Funded at Inria, France

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

Eligible candidate may Apply as soon as possible.

 

(01) PhD Degree – Fully Funded

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

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 : 2023-06-30

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(02) PhD Degree – Fully Funded

PhD position summary/title: PhD Position F/M Markerless 3D localization of surgical tool in a per-operative context

Today, according to Global Market Insights, the orthopedic medical device (MD) market is growing rapidly and will be worth more than $22.4 billion by 2025. Joint replacement (hip, knee, extremities) represents nearly 37% of the market share. These devices include conventional ancillary instruments, custom-made guides, navigation systems, and robotic systems. More recently, augmented reality (AR) navigation systems have been developed. They are recognized for their accuracy, low cost, ease of use, as well as clinical added value. It is in this context that the ANR MARSurg project [2021-2025] aims to implement an innovative surgical navigation solution with high scientific, technological and clinical potentials.   

Deadline : 2023-06-30

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(03) PhD Degree – Fully Funded

PhD position summary/title: 2022-05223 – 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 : 2023-06-16

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(04) 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 : 2023-05-31

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(05) PhD Degree – Fully Funded

PhD position summary/title: 2022-05169 – PhD Position F/M Modeling Kinesthetic and Tactile Properties of Virtual Environments

The goal of this PhD is to propose a complete haptics pipeline that is agnostic on the haptic device used and integrate not only the definition of the haptic properties, but also the interaction characteristics of the user. The rendering of the haptic properties should not only be driven by the virtual object properties, but also by the actions of the user in the virtual environment [Vizcay 2021]. Thus, the interaction capabilities of the user should be taken into account [Dewez 2021]. In particular, avatars, the users’ virtual representation, is becoming ubiquitous in virtual reality applications. In this context, the avatar becomes the medium which enables users to interact with the virtual environment and also the main source of tactile and kinesthetic sensations. Moreover, the user’s avatar can modulate the perceived haptic sensations [Jauregui 2014], enabling a wider range of sensations that the ones provided solely by haptic actuators. Finally, haptic sensations should be congruent with the actual user’s actions in order to avoid the potential generation of an uncanny valley [Berger 2018], which is strongly linked with the notions of presence [Skarbez 2017] and virtual embodiment [Kilteny 2012].  

Deadline : 2023-05-31

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(06) PhD Degree – Fully Funded

PhD position summary/title: 2022-05392 – PhD Position F/M Stochastic modelling of communications networks

 Research on the topics of the ERC NEMO project.

Deadline : 2023-05-31

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(07) PhD Degree – Fully Funded

PhD position summary/title: 2023-05744 – PhD Position F/M Optimization of resources placement in Fog-based IoT systems based on latency analysis

The objective of this thesis is to study the optimization of resource placement in Fog-based IoT systems based on latency measurement, by evaluating the control plane cost of a change in the architecture. It will particularly address the problem of how to identify the origin of a latency issue, and based on this finding, propose an optimization that take into account the cost and elasticity of the control plane.

Deadline : 2023-05-31

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(08) PhD Degree – Fully Funded

PhD position summary/title: 2023-05874 – Doctorant F/H Théorie des bandits pour le suivi personnalisé de patients.

Cette collaboration a pour objectif d’étudier l’exploitation de données de santé pour le suivi personnalisé de patient après intervention chirurgicale. L’espoir est que les données de santé collectées par différents acteurs permettent de passer d’un protocole de suivi identique pour tous les patients à un protocole personnalisé qui, en étant adapté à chaque patient, serait mieux suivi par les patients, avec donc un meilleur bénéfice pour leur santé.

Deadline : 2023-05-31

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(09) PhD Degree – Fully Funded

PhD position summary/title: 2023-05901 – PhD Position F/M Remote attestation for Internet-of-Things swarms

This PhD position is in the context of the Horizon Europe project OpenSwarm, a 40-month research and innovation project coordinated by INRIA Paris and funded by the European Commission. The project aims at developing novel systems of collaborative smart nodes able to interpret the data they generate, and to collaborate in a decentralized manner to communicate efficiently, even in the context of mobility. The technology developed will be validated through 5 use cases in different environments, like agriculture, industry and maritime transport. The project gathers 8 other partners, prestigious universities and industrials in Europe.

Deadline : 2023-05-31

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(10) PhD Degree – Fully Funded

PhD position summary/title: 2023-05942 – Doctorant F/H Modélisation du transfert de dioxygène dans le poumon humain, une approche “contrôle optimal”

Il existe diverses modélisations du poumon humain et de son fonctionnement. Une modélisation communément admise consiste à hiérachiser le système respiratoire en 5 niveaux faisant chacun l’objet d’une modélisation spécifique : Les voies aériennes supérieures (nez-bouche, pharynx, larynx, trachée et les premières générations bronchiques (de 1 à 3), c’est-à-dire les bronches souches et lobaires), les bronches segmentaires (générations 4 à 10), Les bronchioles (générations 11 à 19), Les acini (bronchioles alvéolaires, canaux alvéolaires et sacs alvéolaires, des générations 20 à 23) et la membrane alvéolo-capillaire (échanges gazeux). Ce modèle très complet couple des équations aux dérivées partielles complexes, qui doivent être résolues à différentes échelles puisque l’arbre bronchique présente une géométrie fractale.

Deadline :  2023-05-28

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(11) PhD Degree – Fully Funded

PhD position summary/title: 2023-05981 – PhD Position F/M Verifying timed cybersecurity properties using formal methods

The main objective of the PhD is to study security properties such as opacity through the analysis of Timed Automata.

Deadline : 2023-05-28

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(12) PhD Degree – Fully Funded

PhD position summary/title: 2023-05971 – PhD Position F/M [Campagne CORDI-S] PhD Position – Robust Anomaly Detection in Multimodal Neuroimaging

The aim of this PhD project is to develop innovative computational imaging tools to model abnormalities, defined as deviations from normal variability, from multimodal brain imaging. To that purpose, deep generative models such as variational auto-encoders and generative adversarial networks will be used to generate pseudo-healthy images from real patients’ images for different modalities (magnetic resonance imaging, positron emission tomography). Comparing pseudo-healthy and real images will provide individual maps of abnormalities. The uncertainty of the generation process will be monitored using techniques such as ensemble modelling to provide a measure of confidence. By extracting the abnormal signal from the images, these abnormality maps will assist clinicians in their diagnosis by providing a clear representation of the pathology.

Deadline :  2023-05-24

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(13) PhD Degree – Fully Funded

PhD position summary/title: 2023-05862 – PhD Position F/M Designing highly efficient ultrafast dynamical metasurface for LIDAR applications

Atlantis is  a joint project-team  between Inria and  the Jean-Alexandre Dieudonné Mathematics Laboratory at  Université Côte d’Azur. The team  gathers applied mathematicians and  computational scientists who are collaboratively undertaking  research activities aiming at the design, analysis, development and  application of innovative numerical methods for systems of  partial differential equations (PDEs) modelling nanoscale light-matter interaction problems. In this context, the team is  developing  the   DIOGENeS  [https://diogenes.inria.fr/]  software suite,  which  implements  several Discontinuous  Galerkin  (DG)  type methods tailored to the systems  of time- and frequency-domain Maxwell equations  possibly coupled  to  differential  equations modeling  the behaviour of propagation  media at optical frequencies.  DIOGENeS is a unique  numerical   framework  leveraging   the  capabilities   of  DG techniques  for  the simulation  of  multiscale  problems relevant  to nanophotonics and nanoplasmonics.

Deadline : 2023-05-21

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(14) PhD Degree – Fully Funded

PhD position summary/title: 2022-05130 – 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 : 2023-05-20

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(15) PhD Degree – Fully Funded

PhD position summary/title: 2023-05930 – PhD Position F/M Trustworthy AI hardware architectures

Context and background: Nowadays, there is a growing and irreversible need to distribute Artificial Intelligence (AI) applications from the cloud to edge devices, where computation is largely or completely performed on distributed Internet of Things (IoT) devices. This trend aims to address issues related to data privacybandwidth limitations, power consumption reduction and low latency requirements, especially for real-time, mission- and safety-critical applications (e.g., in autonomous driving, support for gesture and medical diagnosis, smart power grid or preventive maintenance).

Deadline : 2023-05-20

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(16) PhD Degree – Fully Funded

PhD position summary/title: 2023-05918 – Doctorant F/H Sémantique des langages de programmation – logique linéaire ordonnée pour une reconstruction de la gestion des ressources dans Rust

Le doctorant devra étendre les résultats existants sur la logique linéaire ordonnée et les modèles de calcul associés, appliquer cette recherche à la vérification des langages de programmation tels que Rust en donnant une traduction fonctionnelle pour décrire les destructeurs et leur interaction avec les erreurs et exceptions. L’objectif à long terme est de montrer comment les notions liées à la propriété (systèmes de types linéaires, typage de région, unicité) découlent d’une reconstruction rationnelle dans le contexte de la sémantique catégorielle des langages de programmation et comment une vue abstraite informe avantageusement la conception de tels langages.

Deadline : 2023-05-19

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(17) PhD Degree – Fully Funded

PhD position summary/title: 2023-05924 – PhD Position F/M Adaptive security framework for microservices-based cloud applications

This PhD thesis will be carried out in the DiverSE team (https://www.diverse-team.fr/) which is located in Rennes. DiverSE’s research is in the area of software engineering.

Deadline : 2023-05-19

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(18) PhD Degree – Fully Funded

PhD position summary/title: 2023-05796 – PhD Position F/M Pole Vault generic analysis, human motion and optimal interaction

This Phd thesis (PAOLI project) is part of the perspective of monitoring very high-level pole vaulters in the perspective of international events (Olympic games & world championships). In this regard, the project is in line with the expectations of the scientific staffs of French federation responsible for performance support. The scientific developments within the PAOLI project thus may bring innovative training programs and consistent use of in situ data for performance improvement.

Deadline : 2023-05-15

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(19) PhD Degree – Fully Funded

PhD position summary/title: 2023-05893 – PhD Position F/M Full-Body Design and Control of an Aerial Manipulator for Advance Physical Interaction

In this PhD we want to go beyond the current approaches looking at aerial manipulators as a whole system, both from the design and control perspectives. This should allow obtaining more precise and robust aerial manipulators that could be used in more complex task, e.g., in construction sites helping humans for burden operations.

Deadline : 2023-05-15

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(20) PhD Degree – Fully Funded

PhD position summary/title: 2023-05881 – PhD Position F/M Audio-visual Speech Enhancement: Bridging the Gap between Supervised & Unsupervised Approaches

This is a fully-funded PhD position as part of the REAVISE project: “Robust and Efficient Deep Learning-based Audio-visual Speech Enhancement” (2023-2026). REAVISE aims to develop a unified audio-visual speech enhancement (AVSE) framework that robustly integrates acoustic data (noisy speech signal) with accompanying visual information (video of speaker’s lip movements) in order to recover an intelligible, high-quality estimate of the clean speech signal with low computational power and independently of the acoustic and visual noise environments. These objectives will be achieved by leveraging the recent methodological breakthroughs in statistical signal processing, machine learning, computer vision, and deep neural networks.

Deadline : 2023-05-14

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(21) PhD Degree – Fully Funded

PhD position summary/title: 2023-05844 – PhD Position F/M Dynamic Adaptation of Machine Learning and Deep Learning Workflows across the Cloud-Fog-Edge Continuum

The recent spectacular rise of the Internet of Things (IoT) and the associated augmentation of the data deluge motivated the emergence of Edge computing [1] as a means to distribute processing from centralized Clouds towards decentralized processing units close to the data sources. The key idea is to leverage computing and storage resources at the “edge” of the network, i.e., near the places where data is produced (e.g., sensors, routers, etc.). They can be used to filter and to pre-process data or to perform (simple) local computations (for instance, a home assistant may perform a first lexical analysis before requesting a translation to the Cloud). 

Deadline : 2023-05-02

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(22) PhD Degree – Fully Funded

PhD position summary/title: 2023-05854 – PhD Position F/M Knowledge-based reinforcement learning and knowledge evolution [Campagne DOC MI-NF-GRE-2023]

Group: The work will be carried out in the mOeX team common to INRIA & LIG. It is related to the MIAI Knowledge communication and evolution chair. mOeX is dedicated to study knowledge evolution through adaptation. It gathers researchers which have taken an active part these past 15 years in the development of the semantic web and more specifically ontology matching and data interlinking.

Deadline : 2023-05-01

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(23) PhD Degree – Fully Funded

PhD position summary/title: 2023-05856 – PhD Position F/M Cultural knowledge evolution and belief revision [Campagne DOC MI-NF-GRE-2023]

Cultural knowledge evolution deals with the evolution of knowledge representation in a group of agents. For that purpose, cooperating agents interact with their environment and other agents. When these agents find their behaviour inadequate, which can be detected by failing to understand others, they use operators to adapt their beliefs. This framework has been considered in the context of evolving natural languages [Steels, 2012]. We have applied it to ontology alignment repair, i.e. the improvement of incorrect alignments [Euzenat, 2017] and ontology evolution [Bourahla et al., 2021]. We have shown that it converges towards successful communication through improving the intrinsic knowledge quality.

Deadline : 2023-05-01

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(24) PhD Degree – Fully Funded

PhD position summary/title: 2023-05910 – PhD Position F/M [Campagne DOC MI-NF-GRE-2023] Data-driven modeling for nonsmooth dynamical systems. Application to the gravity-driven flows in mountains

TRIPOP is a joint research team of Inria Grenoble Rhône-Alpes and of the Laboratoire Jean Kuntzmann and started in January 2018. The team is mainly concerned with the modeling, the mathematical analysis, the simulation and the control of nonsmooth dynamical systems, with a strong application to modeling natural environmental risks in mountains. Nonsmooth dynamics concerns the study of the time evolution of systems that are not smooth in the mathematical sense, i.e., systems that are characterized by a lack of differentiability, either of the mappings in theirs formulations, or of theirs solutions with respect to time. In mechanics, the main instances of nonsmooth dynamical systems are multibody systems with Signorini’s unilateral contact, set-valued (Coulomb-like) friction and impacts, or in continuum mechanics, ideal plasticity, fracture or damage. The members of the team have a long experience of nonsmooth dynamics modeling together with the development of simulation software.

Deadline : 2023-05-01

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(25) PhD Degree – Fully Funded

PhD position summary/title: 2023-05832 – PhD Position F/M Taking into account metrological aging and network failure inside sensor networks applied to structural monitoring

Structural health monitoring consists in integrating sensors into a structure, typically transportation or energy infrastructures, in order to monitor its health without further knowledge, solely from measurements through a software/hardware system comprising a set of sensors communicating (wired or wirelessly) with a smart supervisor integrating models and decision algorithms, merging and interpreting in-situ data. The usage of this type of system is increasing and some commercial examples can be found: SERCEL’s S-lynk system or HBK Monitoring solutions, etc. Those products generally do not take into account the quality or availability of sensor data over time. However, they are dependent on the quality of the wireless network and the aging of the communication system. While the managers of critical structures (EDF, SNCF, etc.) are waiting, more than ever [3], for reliable and long-term solutions for the control of structures, metrological aging coupled with local failure (in space and/or time) of a part of the network is rarely taken into account [4].

Deadline : 2023-04-30

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(26) PhD Degree – Fully Funded

PhD position summary/title: 2023-05853 – PhD Position F/M Dynamic Parallelization of Sparse Codes for Machine Learning and High-Performance Computing

Specifically, this PhD thesis will investigate how to delay the optimization of sparse code at runtime when the sparse structure is known. Given the dense specification, we aim at producing a code able to specialize itself on the input sparse structure, resulting in a parallel code using state-of-the art linear algebra libraries}. Many issues and trade-offs must be investigated. To quote a few:

Deadline : 2023-04-30

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(27) PhD Degree – Fully Funded

PhD position summary/title: 2023-05984 – PhD Position F/M Differential privacy in federated learning

a) comparison of distributions of the clients’ (high dimensional) data in federated learning in
a differentially private manner to account for the need for a change of the trained models over time
 
b) theoretically dissecting and formalizing the information leakage in classical federated learning framework to develop a foundational perspective towards the state-of-the-art gradient-inversion attacks in federated learning, and
 
c) improving the current state-of-the-art method to assert formal differentially private noise in federated model training by improving the sensitivity of the process.

Deadline : 2023-04-30

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(28) PhD Degree – Fully Funded

PhD position summary/title: 2023-05706 – Doctorant F/H Thèse CIFRE : Modélisation et prédiction du comportement utilisateur face aux emails de phishing

Le phishing reste une des menaces majeures sur Internet[1]. Il est un des moyens privilégiés des attaquants pour s’introduire  dans le réseau d’une entreprise. Il permet de directement compromettre le système informatique en faisant exécuter des pièces jointes malveillantes ou de soutirer des informations sensibles aux utilisateurs. Celles-ci peuvent être des informations privées ou bien des informations de connexion comme des mots de passe permettant ensuite aux attaquants d’usurper leur identité au sein d’une entreprise et mener d’autres actions. D’ailleurs les envois d’email de phishing sont de plus en plus ciblés avec notamment la pratique du spear phishing et le discernement de l’attaque par un utilisateur est de plus en plus complexe. Les emails sont souvent bien rédigés, utilisent des adresses d’expédition moins douteuses à première vue, etc.

Deadline : 2023-04-28

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(29) PhD Degree – Fully Funded

PhD position summary/title: 2023-05911 – Doctorant F/H Modélisation couplant hydrodynamique et transport d’embâcles lors d’inondations

Au sein de l’équipe LEMON à Montpellier, nous souhaitons développer une modélisation couplée (avec rétroaction) de l’hydrodynamique à surface libre avec le transport d’embâcles. Une piste à explorer pour la rétroaction des embâcles sur l’hydrodynamique est d’utiliser les modèles à porosité, que Pascal Finaud-Guyot et Antoine Rousseau co-développent au sein de l’équipe-projet LEMON à Montpellier.

Deadline : 2023-04-23

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(30) PhD Degree – Fully Funded

PhD position summary/title: 2023-05752 – PhD Position F/M [ALRC 2023] Mathematical and numerical analysis of dissipative problems with free boundaries

The PhD thesis enters the framework of a longstanding collaboration of the RAPSODI project team with colleague of the CEA and ANDRA on the mathematical and numerical modeling of iron corrosion. 

The goal of this thesis is the mathematical study (well-posedness, asymptotic behavior) of a simplified model in order to build a better understanding of a more complex model recently developed by our team. Numerical simulations of the simplified model are also expected. The mid-term goal is to transpose the obtained results to the full model.

Deadline :  2023-04-18

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(31) PhD Degree – Fully Funded

PhD position summary/title: 2023-05753 – PhD Position F/M Neurons, Burst and Adaptive Controller/Observer: Induced Contraction

The development of digital technology is no longer sustainable and the end of `Moore’s law’ is now happening. Neuromorphic engineering is a response to this inefficiency of digital machines in comparison to the animal world, in which the bits and clocks of digital computers are replaced by the spikes and rhythms of analog electronic circuits. We believe that by studying the non-intuitive analogy that exists between neurons and classical adaptive controllers: the appearance of bursting with or without injection of white noise, we head for the theory of designing event-based online adaptation, which is energy efficient compared to the digital machines prevalent these days. Moreover, our hypothesis is that bursting is necessary to induce contraction, hence for adaptation, in both examples of neurons and classical adaptive controllers. Thereby, we plan to base our study on the theory of induced contraction, motivated by the phenomenon of noise-induced synchronization of neurons.

Deadline : 2023-04-18

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(32) PhD Degree – Fully Funded

PhD position summary/title: 2023-05754 – PhD Position F/M [ALRC 2023] Synchronization of uncertain and different-type systems via reinforcement learning

The problem of synchronizing different and uncertain dynamical systems (agents) appears in multiple contexts: electrical, mechanical or biological systems, network communications, etc. The difficulty comes from the need to force distinct dynamics (e.g., of mass, size, or even physical nature) to make similar movements by rejecting external disturbances. The uncertainty of the models for the agents introduces another technical obstacle for the realization of synchronous movements. The connection topology (leader-follower, or mutual), and the associated communication delays, represent other complications to synchronization, which must also be neglected by agents. Finally, constraints on allowable movements for each participant (e.g., avoiding collisions) turn this problem into a highly nonlinear and nonconvex question, where conventional control theory fails to provide a solution. The existing methods, mainly related to the model predictive control approach, deal with simple linear models of the systems, with a limited uncertainty, while assuming a convexity of the optimized goal functional, which is rarely observed in practice. The objective of this thesis project is to design synchronization control algorithms under severe uncertainty conditions, with the presence of other uncertain agents, which penalize the risk of error and its cost. The complexity of the posed problem requires an interdisciplinary approach for its solution, as envisaged in this project, which belongs to an intersection of the fields of machine learning and control theory. It is intended to combine reinforcement learning and model predictive control approaches for the realization of synchronization strategies for different and uncertain dynamic agents.

Deadline : 2023-04-18

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(33) PhD Degree – Fully Funded

PhD position summary/title: 2023-05757 – PhD Position F/M [ALRC 2023] Interaction beyond the sensorimotor loop

Knowledge about user behavior under high latency conditions is limited, as is knowledge about how best to design control systems that address these issues. In the field of Human-Computer Interaction (HCI), the effects of latency on user behavior are often studied below 200 ms [Pavlovych 2009], but some performance models successfully describe target acquisition tasks with (fixed) latencies up to 4 seconds [Hoffmann 1992] [Hoffmann 2017]. In this project, we will investigate whether these models are valid with varying or higher latency levels, and how to improve user control in such interactive environments.

Deadline : 2023-04-18

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(34) PhD Degree – Fully Funded

PhD position summary/title: 2023-05756 – PhD Position F/M Using haptic cues to improve micro-gesture interaction

As part of the ANR MIC project, the Ph.D. candidate will work on interaction techniques with micro-gestures. Micro-gestures are hand gestures performed on one hand with the same hand. Examples include tap and swipe gestures performed by one finger on another finger. The Ph.D. thesis aims at improving micro-gesture interaction by leveraging haptic feedback and guidance. The research goal is to design, implement and evaluate interaction techniques based on the innovative combination of micro-gesture and haptic cues for different tasks, starting with pointing and command selection. Later, the candidate will work on specific tasks in an AR/VR environment, emerging from the developed applications in the MIC project, possibly the positioning, rotation, and scaling of 3D objects, as well as text entry. These interaction techniques leverage the sensorimotor loop to foster fast, incremental, reversible, and discoverable interaction.

Deadline : 2023-04-18

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(35) PhD Degree – Fully Funded

PhD position summary/title: 2023-05880 – PhD Position F/M Improved quantum algorithms for NOMA systems

The objective is to improve the current quantum algorithms, to customize them for signal processing algorithms for NOMA transmission systems. In particular, the objective is to improve the performances already obtained with a basic algorithm (which proves that this is a promising approach), and ideally designing new algorithms to reduce the complexity of detection algorithms used in telecommunications, for the separation of multiple signals in large multi-user receivers (large number of antennas, large number of sensors, …). This issue is topical because this scenario is critical for the improvement of massive IoT access systems or for 6G development.

Deadline : 2023-04-16

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(36) PhD Degree – Fully Funded

PhD position summary/title: 2023-05868 – PhD Position F/M CIFRE – Secure, Private and Multi-modal Authentication

Granting access appropriately is the first condition for setting up a secure and well-managed cybersystem. With the advent of ubiquitous computing, the classical login/password paradigm is at times unsuitable. Therefore, we see a rise in alternative authentication methods (e.g., biometric-based or behavioral-based).

The project PUMAS aims at exploring multi-modal authentication methods in a multi-environment setting (desktop, smartphone, wearable, IoT…). The goal is to design an end-to-end machine learning risk-based authentication system with an integrated continuous anomaly and intrusion detection system.

The PhD thesis will be in collaboration between OpenSezam and Inria. The PhD student will be an Open Sezam employee for the duration of the thesis (CIFRE) while the scientific supervision will be handled by the Privatics team of INSA-Lyon/Inria CITI lab.

Deadline : 2023-04-16

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(37) PhD Degree – Fully Funded

PhD position summary/title: 2023-05790 – PhD Position F/M Acoustic to Articulatory Inversion by using dynamic MRI images

Articulatory synthesis mimics the speech production process by first generating the shape of the vocal tract from the sequence of phonemes to be pronounced, then the acoustic signal by solving the aeroacoustic equations [1, 2]. Compared to other approaches to speech synthesis which offer a very high level of quality, the main interest is to control the whole production process, beyond the acoustic signal alone.

The objective of this PhD is to succeed in the inverse transformation, called acoustic to articulatory inversion, in order to recover the geometric shape of the vocal tract from the acoustic signal. A simple voice recording will allow the dynamics of the different articulators to be followed during the production of the sentence.

Deadline :2023-04-15

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(38) PhD Degree – Fully Funded

PhD position summary/title: 2023-05783 – PhD Position F/M Object Detection from Few Multispectral Examples

ATERMES is an international mid-sized company, based in Montigny-le-Bretonneux (France) with strong expertise in high technology and system integration from the upstream design to the long-life maintenance cycle. It specializes in offering system solutions for border surveillance. Its flagship product BARIER(TM) (“Beacon Autonomous Reconnaissance Identification and Evaluation Response”) provides ready application for temporary strategic site protection or ill-defined border regions in mountainous or remote terrain where fixed surveillance modes are impracticable or overly expensive to deploy. As another example, SURICATE is the first of its class optronic ground “RADAR” that covers very efficiently wide field with automatic classification of intruders thanks to multi-spectral deep learning detection.

Deadline :  2023-04-12

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(39) PhD Degree – Fully Funded

PhD position summary/title: 2023-05787 – PhD Position F/M Privacy-preserving decentralized learning through Model fragmentation and Private Aggregation

The WIDE team is involved in a number of projects that tackle related problems. In the context of the SOTERIA H2020, Davide FREY is currently working on decentralized and privacy-preserving machine learning algorithms using trusted execution environments. This thesis provides a complementary approach, and there is thus a concrete possibility to directly apply the results of this Ph.D. thesis to the Personal Data Vault being developed by the SOTERIA project. Davide Frey is also active, with François Taiani, in the FedMalin Inria Challenge, which also investigates decentralized machine learning platforms. In particular, in the context of FedMalin, WIDE is currently developing a library for decentralized machine learning that can be exploited by this thesis. Moreover, we envision close collaboration with other teams involved in the FedMalin project. In addition to the collaborations we mentioned above with the partners of the SOTERIA H2020 project and of the FedMalin project, we are planning to collaborate with Anne-Marie Kermarrec’s group at EPFL.

Deadline : 2023-04-09

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(40) PhD Degree – Fully Funded

PhD position summary/title: 2023-05768 – PhD Position F/M Balancing Performance and Sustainability for FaaS in the Fog

Fog computing is an extension of the traditional cloud computing model in which compute, storage, and network capabilities are distributed closer to users [1]. Fog computing is motivated by the need to support Internet of Things (IoT) applications, such as smart cities and AI-enabled surveillance systems, that have strict demands for bandwidth and low-latency computation. A compelling programming model for developing such applications is the Function-as-a-Service (FaaS) model [2], the core element of serverless computing. FaaS supports easy movement of functions along the cloud-to-thing continuum, allowing optimizing for diverse factors, such as latency and energy efficiency. Moreover, FaaS supports fine-grained, short-lived resource allocations, enabling increased infrastructure utilization.

Deadline :  2023-04-08

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(41) PhD Degree – Fully Funded

PhD position summary/title: 2023-05764 – PhD Position F/M Towards an efficient economic orchestration of 5G and beyond networks

The goal of the thesis is to identify, to formally define the economic relations between partners, and to embed these aspects in the service offering and provisioning and their associated operation and management. The purpose is to investigate the potential need to regulate the relations towards an efficient orchestration, and compliance to existing legislation, particularly the one around network neutrality: is a technological proposition an infringement to the current legislation, and can it be adapted?

Deadline :2023-04-08

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(42) PhD Degree – Fully Funded

PhD position summary/title: 2023-05779 – Doctorant F/H Diagnostiquer l’acoustique d’une salle grâce au traitement du signal et à l’apprentissage automatique

Verrou 1 : Directivité et information géométrique

Nos études préliminaires ont permis de développer des méthodes qui, dans des conditions idéalisées, permettent d’estimer les profils d’absorption des parois étant donnée la géométrie de la pièce d’une part [1,2,3], et d’estimer la géométrie d’une pièce aux parois idéales d’autre part [4]. Un verrou majeur est la généralisation de ces méthodes à des sources, microphones et parois réels, c’est-à-dire, dont leurs réponses dépendent de la fréquence et de l’angle, ainsi que l’hybridation de ces approches pour pallier à une connaissance imparfaite de la géométrie dans les cas d’usages réels.

Verrou 2 : Guidage hybride par la physique et les données

L’état de l’art se scinde en deux types d’approches : celles purement guidées par les données annotées simulées, consistant à entraîner un modèle type réseau de neurones, et celles purement guidées par la physique, résolvant par optimisation un problème inverse reposant sur un modèle acoustique idéalisé. Un verrou important consiste à tirer le meilleur de ces deux paradigmes en les hybridant. Cela passera par l’amélioration du réalisme physique des simulateurs, l’utilisation de techniques dites auto-supervisé sur données non-annotées, et l’emploi de techniques dites d’unrolling pour corriger les modèles physiques sous-jacents par apprentissage.

Verrou 3 : Diffusion et géométries complexes

Les approches en acoustique des salles, d’intérêt pour nos travaux, considèrent uniquement des boîtes aux parois parfaitement spéculaires et réfléchissantes. Il y a donc un verrou fort à lever d’un point de vue de la modélisation acoustique pour généraliser ce modèle simple à des situations plus proches de la réalité, incluant géométries complexes et phénomènes de diffusion aux parois. Bien que d’importantes bases théoriques existent sur différents aspects de cette modélisation, leur combinaison en un modèle tractable et adapté aux réalités de l’acoustique du bâtiment reste ouverte.

Deadline :2023-04-08

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(43) PhD Degree – Fully Funded

PhD position summary/title: 2022-05436 – 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 : 2023-04-01

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(44) PhD Degree – Fully Funded

PhD position summary/title: 2022-05275 – PhD Position F/M Self-supervised learning for implicit shape reconstruction

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

Deadline : 2023-03-31

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

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

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

 

 

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