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 Deep Neural Network-assisted computational design of highly efficient ultrafast dynamical metasurfaces
The present doctoral project is part of a collaborative project between the Atlantis project-team from the Inria Research Center at Université Côte d’Azur and the CNRS-CRHEA laboratory in Sophia Antipolis, France.
Atlantis is a joint project-team between Inria and the Jean-Alexandre Dieudonné Mathematics Laboratory at Université Côte d’Azur. The team gathers applied mathematicians and computational scientists who are collaboratively undertaking research activities aiming at the design, analysis, development and application of innovative numerical methods for systems of partial differential equations (PDEs) modelling nanoscale light-matter interaction problems. In this context, the team is developing the DIOGENeS [https://diogenes.inria.fr/] software suite, which implements several Discontinuous Galerkin (DG) type methods tailored to the systems of time- and frequency-domain Maxwell equations possibly coupled to differential equations modeling the behaviour of propagation media at optical frequencies. DIOGENeS is a unique numerical framework leveraging the capabilities of DG techniques for the simulation of multiscale problems relevant to nanophotonics and nanoplasmonics.
The Research Center for Heteroepitaxy and its Applications (CRHEA) is a CNRS research laboratory. The laboratory is structured around the growth of materials by epitaxy, which is at the heart of its activities. These materials are grouped today around the theme of high bandgap semiconductors: gallium nitrides (GaN, InN, AlN and alloys), zinc oxide (ZnO) and silicon carbide (SiC). Graphene, a zero bandgap material, epitaxially grown on SiC, completes this list. Different growth methods are used to synthesize these materials: molecular beam epitaxy (under ultrahigh vacuum) and various vapor phase epitaxies. Structural, optical and electrical analysis activities have been organized around this expertise in epitaxy. The regional technology platform (CRHEATEC) makes it possible to manufacture devices. In terms of applications, the laboratory covers both the field of electronics (High Electron Mobility Transistors, Schottky diodes, tunnel diodes, spintronics, etc.) and that of optoelectronics (light-emitting diodes, lasers, detectors, materials for nonlinear optics, microcavity structures for optical sources, etc.). The laboratory has also embarked on the “nano” path, including both fundamental aspects (nanoscience) and more applied aspects (nanotechnology for electronics or optics).
Deadline : 2024-12-31
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD student on privacy-preserving federate learning with applications in oncology
This PhD student position will be supported by the HE Trumpet project, the HE Flute project and/or the PEPR IA Redeem project. While this position will be in the MAGNET team in Lille, we will collaborate with the several European project partners.
While AI techniques are becoming ever more powerful, there is a growing concern about potential risks and abuses. As a result, there has been an increasing interest in research directions such as privacy-preserving machine learning, explainable machine learning, fairness and data protection legislation.
Privacy-preserving machine learning aims at learning (and publishing or applying) a model from data while the data is not revealed. Statistical privacy allows for bounding the amount of information revealed.
The MAGNET team is involved inthe related TRUMPET, FLUTE and REDEEM projects, and is looking for team members who can in close collaboration with other team members and national & international partners contribute to one or more of these projects. All of these projects aim at researching and prototyping algoirhtms for secure, privacy-preserving federated learning in settings with potentially malicious participants. The TRUMPET and FLUTE projects focus on applications in the field of oncology, while the REDEEM project has no a priori fixed application domain.
Deadline : 2024-12-31
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD position F/M Building physics-based multilevel surrogate models from neural networks. Application to electromagnetic wave propagation
Numerical simulations of electromagnetic wave propagation problems primarily rely on a space discretization of the system of Maxwell’s equations using methods such as finite differences or finite elements. For complex and realistic three-dimensional situations, such a process can be computationally prohibitive, especially when the end goal consists in many-query analyses (e.g., optimization design and uncertainty quantification). Therefore, developing cost-effective surrogate models is of great practical significance.
There exist different possible ways of building surrogate models for a given system of partial differential equations (PDEs) in a non-intrusive way (i.e., with minimal modifications to an existing discretization-based simulation methodology). In recent years, approaches based on neural networks (NNs) and Deep Learning (DL) have shown much promise, thanks to their capability of handling nonlinear or/and high dimensional problems. Model-based neural networks, as opposed to purely data-driven neural networks, are currently the subject of intense research for devising high-performance surrogate models of parametric PDEs.
The concept of Physics-Informed Neural Networks (PINNs) introduced in [1], and later revisited in [2], is one typical example. PINNs are neural networks trained to solve supervised learning tasks while respecting some given physical laws, described by a (possibly nonlinear) PDE system. PINNs can be seen as a continuous approximation of the solution to the PDE. They seamlessly integrate information from both data and PDEs by embedding the PDEs into the loss function of a neural network. Automatic differentiation is then used to actually differentiate the network and compute the loss function.
Following similar ideas, and relying on the widely known result that NNs are universal approximators of continuous functions, DeepONets [3] are deep neural networks (DNNs) whose goal is to learn continuous operators or complex systems from streams of scattered data. A DeepONet consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). PINNs and DeepONet are merely two examples of many DNNs that have contributed to making the field of Scientific Machine Learning (SciML) so popular in recent years.
Deadline : 2024-11-30
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Scalable Translation Validation for High-Performance Computing and Machine Learning
The PhD thesis will be held at Ecole Normale Supérieure (ENS-Lyon), in Lyon, France. ENS-Lyon is one of the top public universities in France and its ranked among the best universities in the world (QS world university ranking: 184).
The PhD student will be an employee of Inria, the French National Research Institute of Research in Computer Science which covers a wide spectrum of research in Computer Science.
This PhD thesis is within a collaboration framework between Inria Lyon and Iowa State University (USA).
Deadline : 2024-11-30
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD student M/F Bandit theory for personalized patient monitoring.
The BIP-UP project funded by the ANR begins on January 1, 2023 and will last 4 years. BIP-UP is based on a collaboration that has existed for several years between the Inria Scool team, led by Philippe Preux and the INSERM 1190 unit located at the Lille University Hospital, led by François Pattou, both professors at the University of Lille. The
aim of this collaboration is to study the use of health data for personalized patient monitoring after surgery. The hope is that the health data collected by different stakeholders will make it possible to move from an identical monitoring protocol for all patients to a personalized protocol which, by being adapted to each patient, would be better followed by patients, with therefore a better benefit for their health.
Started in 2019, this collaboration has already borne fruit. On the one hand, these 3 years of work have allowed us to better identify and characterize the problems to be addressed and ways to address them. On the other hand, in addition to permanent staff in both teams, an engineer, a doctoral student and a post-doc have carried out significant preliminary work.
A particularly exciting aspect of this work context is the anchoring in the field since the INSERM team is in direct contact with patients (consultation, operation, follow-up) and is of course an expert on the medical aspects related to the pathology operated on.
Deadline : 2024-11-30
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD Semantically-enriched queries and analysis of metagenomic datasets
Genomic data enable critical advances in medicine, ecology, ocean monitoring, and agronomy. A major limitation is that it is impossible to query these entire data (petabytes of sequences).
The OmicFinder project (https://project.inria.fr/omicfinder/) will provide a search engine able to remove this lock. The central algorithmic idea of a genomic search engine is to index and query small exact words (hundreds of billions over millions of datasets), as well as the associated metadata. The project brings together Inria teams in algorithmic on strings, ontologies, computing architectures, and data distribution. They will bring algorithmic advances including computation frugality, clever index distributions, and smart ontology-based questions and answers filtration.
The core idea of the OmicFinder is to build an index of small exact words present in millions of datasets, so that a query based on this index will return the list of datasets that have (at least) a sequence containing this word. This corresponds to the syntactic aspect of query resolution.
Deadline : 2024-11-03
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Decentralised Market-based Application Orchestration in Fog and IoT Environments
In the context of the TARANIS project (PEPR Cloud), we are offering a PhD position to investigate flexible and decentralised application orchestration in fog environments.
The work will be carried out within the MAGELLAN team (Inria Centre at Rennes University, IRISA) at Rennes. Rennes is the capital city of Brittany, situated in the western part of France. Well-connected to Paris via a high-speed train line, Rennes is a lively city and a major center for higher education and research. The work will involve close collaboration with the STACK team (IMT Atlantique, Inria, LS2N) at Nantes.
Deadline :2024-11-01
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Aerial Robots with the sense of touch
The Inria Centre at Rennes University is one of Inria’s eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
Deadline : 2024-10-31
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Sensors-based Distributed Control of Multi-Drone Systems for Agile Cooperative Aerial Manipulation
- The work will be carried in English in the Rainbow team at the Inria Rennes Bretagne Atlantique research center.
- The Ph.D. position is full-time for 3 years (standard duration in France). The position will be paid according to the French salary regulations for PhD students.
- We do high quality and impactful research in robotics, publishing on the major journals and conferences.
- We often collaborate with other top researchers in europe and worldwide.
- You will have access to a well established laboratory including:
- two flying arenas equipped with motion tracking system, several quadrotors, and a few fully-actuated manipulators,
- one robotic manipulation lab equipped with several robotic arms, like the Franka Emika Panda.
- You will be part of an international and friendly team. We organize several events, from after works, to multi-day lab retreat.
- Regular visits and talks by internationally known researchers from top research labs.
Deadline : 2024-10-31
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M REAL-TIME SURGICAL ASSISTANT FOR DISSECTION GUIDANCE
Inria, the French National Institute for Computer Science and Applied Mathematics, promotes “scientific excellence for technology transfer and society”. Graduates from the world’s top universities, Inria’s 2,700 employees rise to the challenges of digital sciences. With its open, agile model, Inria can explore original approaches with its partners in industry and academia and provide an efficient response to the multidisciplinary and application challenges of digital transformation. Inria is the source of many innovations that add value and create jobs.
Our project is to develop a real-time surgical assistant that leverages AI and augmented reality to provide precise dissection guidance, enhancing surgical accuracy.
Deadline : 2024-10-31
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Anticipating Human Behavior for Human-Robot Interaction
Collaboration between Inria team THOTH and the multidisciplinary institute in artificial intelligence (MIAI) in Grenoble
Research activities in MIAI aim to cover all aspects of AI and applications of AI with a current focus on embedded and hardware architectures for AI, learning and reasoning, perception and interaction, AI & society, AI for health, AI for environment & energy, and AI for industry 4.0.
The close connection to University of Grenoble Alpes offers additionally many oportunities for collaboration, further training and networking within and cross research fields.
Deadline : 2024-10-13
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Quantification of security vulnerabilities caused by heavy code reuse through package managers and library dependencies
The doctoral project is part of the SWHSec project. It will be supervised by Clémentine Maurice and Pierre Laperdrix, both CNRS researcher in the Spirals team.
The objective of the SWHSec project is to explore several of the new possibilities offered by the availability of Software Heritage to blend together the “vertical” and “horizontal” approaches to software supply chain security.
Deadline : 2024-10-13
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Latency-Constrained Communications for Multi-Tier Computation
This PhD position will be funded by the ANR JCJC project “Tailoring Communications in Multi-Tier Computation for Digital Twinned Process Control” commencing in late 2024 and carried out within the Inria MARACAS team. MARACAS is a research group consisting of approximately 15 people within Inria and INSA Lyon, this includes 6 PhD students, 2 postdocs, and 2 research engineers. The focus of MARACAS is in the theory, algorithms, and experimentation for communication systems, developing and applying methods in information theory, statistical signal processing and machine learning.
The PhD position will complement on-going projects on goal-oriented communications and learning currently being carried out within MARACAS. International collaborations are envisaged, involving researchers at UCLouvain, Czech Technical University in Prague, or the University of California Santa Barbara. National and international research visits and conference attendance are also envisaged with travel expenses covered by the project.
Deadline :2024-10-09
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Smart IoT networks for a sustainable manufacturing as a service paradigm in Industry 4.0
The potential offered by the abundance of sensors, actuators and communications in IoT is hindered by the limited computational capacity of local nodes, making the distribution of computing in time and space a necessity. Several key questions need to be answered to jointly exploit the network, computing and storage resources optimally, accounting at the same time for the trade-offs guaranteeing feasibility, sustainability and the generation of valuable insights. Our research takes upon these challenges, by dynamically distributing resources with the varying demand flow and available assets.
This position falls in the context of the HE UniMaas Project which aims to deliver a platform for flexible, agile and decentralized manufacturing, embracing the MaaS paradigm (Manufacturing as a service). UniMaaS will be built on five main technological pillars: (a) unified on-demand modelling of manufacturing resources and supply chains; (b) intent-based servitization and AI-based estimators; (c)Manufacturing Data Spaces facilitating the interoperable and trustworthy resource servitization; (d) flexible decision making for reconfigurable, circular and sustainable next-generation manufacturing execution systems (MES); and (e) cutting-edge digital technologies for Cloud Manufacturing (CMfg).
In this context, the FUN team is in charge of proposing self-organizing IoT networks allowing the collection of sensitive and real-time data showing the status of manufacturing resources in supply chain as well as the distribution of local decision-making processes based on available resources within the network (IoT – Edge).
Deadline : 2024-10-06
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Inverse magnetisation problem in the paleomagnetic context
The Inria center at Université Côte d’Azur includes 42 research teams and 9 support services. The center’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d’Azur, CNRS, INRAE, INSERM …), but also with the regional economic players.
With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d’Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.
Deadline : 2024-10-01
(16) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Jeux répétés et apprentissage séquentiel : vers des algorithmes équitables et performants
La thèse se place à l’intersection des jeux répétés et de l’apprentissage statistique, et ce dans le but de développer des algorithmes robustes et équitables.
Beaucoup d’algorithmes d’apprentissage fonctionnent séquentiellement (robotique, agent conversationnel, applications avec interactions humaines etc). La théorie des jeux fournit un cadre d’analyse des interactions, duquel peuvent être tirés des algorithmes simples et robustes à appliquer, notamment dans un cadre multi-agents. Il est alors possible de fournir des algorithmes avec des garanties théoriques qui permettent de s’assurer du bon fonctionnement des algorithmes implémentés en pratique.
La théorie des jeux répétés date de la moitié du 20e siècle, elle est particulièrement adéquate pour l’étude de l’apprentissage séquentiel. Ainsi, les jeux répétés asynchrone en sont une extension directe; de même pour la théorie de l’approchabilité, et les algorithmes de matching. Les outils qu’elle a développés seront mis au contact d’autres parties des mathématiques, pour répondre à de nouvelles questions. En particulier, nous nous intéresserons à développer des algorithmes équitables et/ou robustes aux manipulations stratégiques pour différentes applications, incluant les réseaux de télécommunication cognitifs, les algorithmes d’appariement et les algorithmes de prédiction.
Deadline : 2024-09-30
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Topology Design for Decentralized Federated Learning
This PhD thesis is in the framework of Inria research initiative on Federated Learning, FedMalin https://project.inria.fr/fedmalin/.
The PhD candidate will join NEO project-team https://team.inria.fr/neo/.
NEO is positioned at the intersection of Operations Research and Network Science. By using the tools of Stochastic Operations Research, the team members model situations arising in several application domains, involving networking in one way or the other.
The research activity will be supervised by
- Giovanni Neglia, http://www-sop.inria.fr/members/Giovanni.Neglia/index.htm
- Aurélien Bellet, http://researchers.lille.inria.fr/abellet/
Deadline : 2024-09-30
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Energy-efficient QoE-aware Beyond 5G Future Mobile Networks
The Inria centre at Université Côte d’Azur includes 37 research teams and 8 support services. The centre’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d’Azur, CNRS, INRAE, INSERM …), but also with the regiona economic players.
With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d’Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.
Deadline : 2024-09-30
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Signal processing based on the squared eigenfunctions of the Schrodinger operator: Mathematical analysis and application to the identification of Vulnerable Carotid Plaques using CT Scans
The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .
The centre has 40 project teams , 32 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.
Deadline : 2024-09-30
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(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Forming carbon fiber fabrics by visual servoing
As part of the PERFORM program, IRT Jules Verne and the Rainbow team of the Inria centre at Rennes University are offering a PhD thesis entitled “Forming carbon fiber fabrics using visual servoing”.
Deadline :2024-09-30
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Efficient Space and Garbage Collection for Functional Languages and Lambda Calculi
The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris.
The centre has 40 project teams , 32 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.
Deadline : 2024-09-30
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Wave propagation in unbounded hyperbolic media
Hyperbolic metamaterials are artificially engineered anisotropic materials which exhibit some unusual properties, such as negative refraction and backward wave propagation.
The name ‘hyperbolic’ comes from the respective dispersive curves (which relate the frequency and the wave vector of the plane waves propagating in such media): these curves take a form of hyperbolae, rather than circles or ellipses. This property enables them to support, for a fixed wavelength, an arbitrary large wavenumber. Their applications include enhanced particle absorption, emission, and collection, e.g. for sensors and antennas; super-resolution imaging; stealth technologies; rogue wave generation etc.
Unlike isotropic metamaterials, media with hyperbolic dispersion exist in nature, examples including crystals of hexagonal boron nitride, bismuth telluride, or even cold plasma.
From the mathematical point of view, the main particularity of the corresponding models lies in the fact that in the frequency domain the respective problem is (think of the wave equation where the time is replaced by a spatial variable), at least for a range of frequencies. This is strikingly different from classical frequency-domain problems, which are \textbf{elliptic} (think of the Laplace equation). Despite the abundance of the physics literature on this subject, to our knowledge, there exist very few works on the mathematical justification of the hyperbolic metamaterial models. An important related work is a very recent theoretical paper on the Poincar\’e problem (see Dyatlov et al. 2023)
Deadline : 2024-09-30
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Construction de préconditionneurs efficaces pour des problèmes d’imagerie ultrasonore
Ce projet de thèse se situe à l’interface de plusieurs domaines des mathématiques appliquées : étude des équations aux dérivées partielles à coefficients stochastiques, problèmes inverses, analyse et simulation numé- rique. L’équipe d’encadrement regroupe les compétences dans chacune des dif- férentes composantes et s’appuie notamment sur l’expertise de Laure Giovan-
gigli en imagerie médicale et propagation d’ondes en milieux multi-échelles et aléatoires [fliss2020time, boucartmodelisation, Garnier2023] ; combinée à celle de Frédéric Nataf et d’Emile Parolin en calcul haute performance, méthodes d’éléments finis et de décomposition de domaine [Dolean2015, Spillane2014, Bouziani2023, Daas2024, Parolin2020, Claeys2022, Claeys2022b, Nataf2024]. Les problématiques que nous souhaitons résoudre dans ce projet sont apparues lors d’une précédente thèse en imagerie ultrasonore en partenariat avec l’équipe d’Alexandre Aubry de l’Institut Langevin, et particulièrement grâce à l’étroite collaboration entre Laure Giovangigli et Pierre Millien. Enfin la thèse pourra profiter des ressources en calcul haute performance de l’équipe Inria Alpines à laquelle Emile Parolin et Frédéric Nataf appartiennent.
Deadline : 2024-09-30
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD student M/F Biological neural networks with discharges: geometry of interactions and long-time behavior
The Inria Saclay research center was created in 2008. Its dynamics are part of the development of the Saclay plateau, in close partnership on the one hand with the Paris-Saclay University cluster and on the other hand with the Institut Polytechnique de Paris cluster . In order to build an ambitious site policy, the Inria Saclay center signed strategic agreements with these two privileged territorial partners in 2021.
The center has 40 project teams , 32 of which are shared with the University of Paris-Saclay or the Institut Polytechnique de Paris. Its action mobilizes more than 600 people , scientists and research and innovation support staff, from 54 nationalities.
The Inria Saclay – Île-de-France center is a key player in digital science research on the Saclay plateau. It embodies the values and projects that make Inria unique in the research landscape: scientific excellence, technology transfer, multidisciplinary partnerships with institutions with skills that complement our own, in order to maximize Inria’s scientific, economic and societal impact.
Deadline : 2024-09-30
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Detection of coordinated influence campaigns online
The thesis is financed by the newly created agency: Agence ministérielle pour l’intelligence artificielle de défense (AMIAD), and it will be in collaboration with Inria and Ecole Polytechnique.
Deadline : 2024-09-30
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Type-based security properties assurance in operating systems
The Inria Rennes – Bretagne Atlantique Centre is one of Inria’s eight centres and has more than thirty research teams. The Inria Center is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
Deadline : 2024-09-30
(27) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Fast Optimal Transport for the Encoding and Decoding of Brain Activity across species
The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .
The centre has 40 project teams , 32 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.
Deadline : 2024-09-30
(28) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Measuring brain microstructure through myelin content modelling in neurodegenerative diseases
In the context of neurodegenerative disorders, the myelin sheaths surrounding the axons are affected by physiopathological processes. Several techniques have been proposed to measure myelin content with MR imaging relying on different acquisition techniques on the one hand and quantitation techniques on the other hand. One popular acquisition technique is T2 relaxometry [1] but T2* relaxometry (GRASE) [2] is also an interesting surrogate. Other acquisition techniques are proposed such as in (ihMT), which focuses on the dipolar relaxation [3].
T2 relaxometry measures transverse relaxation times using a multi-echo spin echo sequence that provides a set of images at a fixed sampling rate along an exponential decay curve. The shorter the echo times and the longer the echo train length (number of echoes) the better we can characterize the signal decay and disentangle the contribution of the different tissues. T2* relaxometry uses more rapid sequences with GRASE readout schemes to achieve shorter echo times (sampling rate) yet under the influence of magnetic field inhomogeneities.
Considering the fact that the brain possesses several water pools with various T2, resp. T2* constants, many algorithms have been developed to quantify these distributions and the fraction of the different pools [4] notably the fraction of myelin in each voxel [5].
Among the different existing quantitation methods, some postulate that brain tissue can be separated in three different pools: water trapped in the myelin sheaths (T2<40ms), intra-extra cellular water (40<T2<100ms) and cerebrospinal fluid (CSF) (T2>1s), that can be modeled as Gaussians with a mean T2 and standard deviation[6] . Some other methods consider 40 compartments and are less dependent on the initialization, i.e. the T2 distribution a priori [7].
To estimate those different multicompartment models, some regularization is necessary. All these algorithms have in common that they fix a large number of Diracs along the T2 spectrum and estimate the weight of each of the pikes, usually through a non-negative least squares method (NNLS) [8]. Nagtegaal et al. proposed also to add a sparsity constraint on NNLS algorithm to restrict T2 distribution, decreasing the noise impact and improving computation time by limiting the number of components per voxel processed [9].
Interestingly, similar questions arise when characterizing other biological samples such as plants. In this case, multi-exponential T2 MRI is used to extract information about the distribution of water in the main subcellular compartments (vacuole, cytoplasm, cell wall) of the tissue [12], [13]. This information is very important for quality control during storage, drying and processing of plants. In the context of biological applications, a dedicated MR pulse sequence, enabling sampling from the beginning to the end of the decay curve, has been developed on the PRISM platform [14] to increase the accuracy of tissue characterisation. Adaptations of the sequence allow optimizing the radiofrequency pulse, add crusher gradients to annihilate the stimulated echoes and increase the number of echoes. The aim is to improve and test the quantification algorithms and then integrate them into existing software solutions.
Deadline : 2024-09-30
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Reconstructing qualitative macroscopic indicators for highly oscillating media
The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .
The centre has 40 project teams , 32 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.
Deadline : 2024-09-30
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Video analysis to detect relapse in the context of addiction addiction
Inria, the French National Institute for Computer Science and Applied Mathematics, promotes “scientific excellence for technology transfer and society”. Graduates from the world’s top universities, Inria’s 2,700 employees rise to the challenges of digital sciences. With its open, agile model, Inria can explore original approaches with its partners in industry and academia and provide an efficient response to the multidisciplinary and application challenges of digital transformation. Inria is the source of many innovations that add value and create jobs.
Deadline : 2024-09-30
(32) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Hate Speech, Toxicity and Opinions Detection and Analysis on Social Media Conversations
The PhD will be supervised by Chloé Clavel at Inria (Inria Paris centre) in the ALMAnaCH project-team (http://almanach.inria.fr/index-en.html) and Jean-Philippe Cointet (Medialab Sciences Po). It will be financed by the ANR Sinnet project.
Deadline :2024-09-30
(33) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Techniques diagrammatiques pour le calcul fermionique et les probl`emes de comptage.
Les récents progrès en ingénierie quantique laissent espérer une implémentation à moyen terme des avantages computationnels importants que promet le calcul quantique. Cependant, la rareté des ressources en calcul quantique qui seront disponibles dans un premier temps rend cruciale l’identification précises des phénomènes responsables des gains en vitesse de calcul. Ceux-ci s’avèrent subtils, par exemple, la seule présence de superposition ou d’intrication n’est absolument pas suffisante pour garantir un avantage sur le calcul classique.
Plus précisément, les algorithmes quantiques sont le plus souvent décrits par des circuits quantiques à n entrées et m sorties ayant pour sémantique une matrice comportant 2n × 2m coefficients complexes. Dès lors, l’émulation sur un ordinateur classique d’un circuit quantique semble ne pouvoir se faire qu’en temps exponentiel. Pourtant, il existe des cas ou` l’exploitation de structures mathématiques supplémentaires peut nous permettre d’obtenir des algorithmes efficaces. Deux exemples paradigmatiques sont :
1. les circuits stabilisateurs, dont la simulabilité en temps polynomial est assurée par le théorème de Gottesman-Knill et l’algorithme des tableaux
2. les matchgates, définies par Valiant, et dont la simulabilité se réduit au comptage de couplages parfaits dans des graphes planaires via l’algorithme de Fisher-Kasteleyn-Temperley.
Les circuits stabilisateurs ontété particulièrement étudiés dans les dernières décennies, menant aux développement d’une riche théorie mathématique qui as porté de nombreux fruits, notamment la théorie desétats graphes ou le développement de codes correcteurs d’erreurs quantiques.
De leurs côté, les matchgates n’ont pas bénéficier de la même attention. En effet, bien que possédant d’intéressantes propriétés combinatoires, les matchgates semble moins adaptées au modèle usuel de calcul quantique. Le contexte naturel pour elles est le calcul fermionique, un autre modèle de calcul pluséloigné des propositions actuelles de hardwares quantiques.
Deadline : 2024-09-29
(34) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Controlled generation for biais mitigation and cultural awareness in conversational language models
The PhD will be supervised by Benoît Sagot and Chloé Clavel at Inria (Inria Paris centre) in the ALMAnaCH project-team (http://almanach.inria.fr/index-en.html). It will be financed by Benoît Sagot’s chair in the PRAIRIE-PSAI institute and by the ANR Sinnet project.
Deadline :2024-09-28
(35) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Outcome prediction of liver tumor ablation therapy using an AI-driven digital twin
The MIMESIS team is at the forefront of innovation, working in the fields of scientific computing, machine learning, medical imaging, and control. We are an interdisciplinary team, that collaborates closely with clinicians to develop new technologies that can help improve healthcare, in particular through computer-assisted interventions. Our core research activities take place in the biomechanical modeling of soft tissue and developing novel numerical methods for real-time computation. Our research results enable the creation of digital twins of organs for personalized planning, augmented reality during surgery, and control in medical robotics.
The MIMESIS team recently joined the MEDITWIN consortium, whose main objective is to enable doctors to simulate the outcome of various treatment scenarios for a patient. MEDITWIN will enable the clinical validation, and possible industrialization, of these innovations so that these technologies can be deployed in a standardized way, and benefit as many people as possible. The best standards of care will be incorporated into virtualized experiences made accessible worldwide, setting a new benchmark for quality in healthcare and providing a decisive learning ground for progress in medical science. The benefits of digital twins will be assessed for medical teams, patients, and the healthcare system, notably in terms of improving the efficiency of care, quality of multidisciplinary decision-making, and effectiveness and safety of medical practices and interventions. More information about the project and Ph.D. topic can be found here.
Deadline :2024-09-27
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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