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 Modeling and Simulation of Exascale Storage Systems
This thesis is placed in the context of NumPEx (https://numpex.fr/), a key national project whose goal is to co-design the software stack for the exascale era and prepare applications accordingly. This thesis will be co-supervised by Inria and CEA, respectively the Inria center at the University of Rennes and the CEA center at Bruyères-Le-Châtel, near Paris. Beyond the supervision, collaborations within NumPEx with the different partners of the consortium are to be expected.
Deadline : 2024-12-31
(02) 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
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M 15 PhDs on Tensor Modelling, Geometry and Optimisation
TENORS (Tensor modEliNg, geOmetRy and optimiSation) is a Marie Skłodowska-Curie Doctoral Network / Joint Doctorate (2024-2027), offering 15 PhD positions.
The objective of TENORS is to conduct advanced research that addresses critical challenges in the fields of tensor modeling and computation, joining forces from algebraic geometry, global optimisation, numerical computation, high performance computing, data science, quantum physics.
It aims to feed an innovative and ambitious joint-PhD program to train highly qualified young scientists in new scientific and technological knowledge. The PhD candidates will obtain joint/double Phd diplomas from reputed universities within TENORS project.
Deadline :2024-12-31
(04) 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
(05) 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
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
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M First Class Optimisations: Code Transformations as Libraries with Partial Evaluation and Analytic Macros
The PhD will take place in the CASH team, in LIP, Lyon, France. It will be supervised by Gabriel
Radanne, Inria researcher, specialist in compilation of high level languages. It will be made in collaboration with
Richard Membarth, Professor at DFKI-Saarbrücken, Germany.
Deadline : 2024-10-23
(07) 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.
Deadline :2024-09-30
(08) 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
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(09) 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
(10) 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
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(11) 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
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(12) 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
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD Thesis: Privacy-Enhancing Tools for Content Sanitization Using Large Language Models – Application to School Bullying and Participatory Testimony Collection
This PhD thesis project is part of the French Priority Research Program and Equipment (PEPR) on Cybersecurity, interdisciplinary Project on Privacy (iPoP) project involving several French research teams working on data protection, from Inria, universities, engineering schools and the CNIL (French National Commission on Information Technology and Civil Liberties). The PhD is proposed by the PETSCRAFT project-team joint between Inria Saclay and INSA CVL, which tightly collaborate in this large initiative on modeling privacy protection concepts and on the design and deployment of explicable and efficient Privacy-Enhancing Technologies (PETs).
Deadline : 2024-09-30
(14) 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
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Deep learning for assisting clinical decisions in brain imaging: trustworthy validation and benchmarking
The ARAMIS lab, which is also part of Inria (the French National Institute for Research in Computer Science and Applied Mathematics), is dedicated to the development of new machine learning and statistical approaches for the analysis of large neuroimaging and clinical data sets.
This PhD is funded as part of the Paris Artificial Intelligence Research Institute (PRAIRIE – https://prairie-institute.fr/). O. Colliot holds a research Chair within the PRAIRIE institute. Within the PRAIRIE Institute, the PhD candidate will have access to a rich scientific environment covering all aspects of AI, including many seminars, workshops and gatherings for PhD candidates and postdocs.
To perform large scale experiments, the PhD candidate will have access to the Jean Zay supercomputing infrastructure which comprises about 2,000 V100 GPUs and about 400 latest generation A100 GPUs.
The PhD candidate will be interacting frequently with other PhD students as well as with engineers working on the ClinicaDL software platform. In particular, the PhD candidate will receive the help of engineers for data management and implementation.
Deadline : 2024-09-27
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD F/H Shape analysis of microstructure-augmented whiter matter fascicles
Magnetic resonance imaging (MRI) and in particular diffusion MRI (dMRI) provide detailed information about the macroscopic organisation of brain white matter (WM) fiber bundles (see Figure), with a method called fiber tractography. Complementary to the geometry of fibers, dMRI is also sensitive to the microscopic tissue structure and its alteration with pathology. The joint analysis of white matter fascicles and their associated microstructure organisation requires the development of specific mathematical representations.
Deadline : 2024-08-31
(17) 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 : 2024-08-31
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Computer-Assisted Collaborative Design of Transit Maps
The Ph.D. topic lies at the intersection of Human-Computer Interaction and Information Visualization. It will be a paternship between Inria Saclay & Université Paris-Saclay (Theophanis Tsandilas) and the École Centrale de Lyon (Romain Vuillemot). During the thesis, we envision stays at both sites.
Deadline : 2024-08-31
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Generation and control of virtual manikins using machine learning for the simulation of industrial processes using virtual reality
The thesis focuses on the simulation of virtual manikins in an industrial context using virtual reality. The virtual operator is meant to realize different tasks (manipulate, screw…) in virtual environments with different levels of constraints. The movements of the operator must be as faithful as possible to reality, in terms of posture, efforts and interactions with the environment.
Considering the sophistication of the gestures to reproduce and the number of parameters to define manually, it becomes too complex to use classical control methods. In the literature, studies using imitation learning methods show promising results. These methods however have important drawbacks, such as the use of big samples databases and important training times.
The aim of the thesis is to bring substantial modifications to existing methods and to propose a new one that can learn and coordinate, using a database of moderate size, movements and interactions of a virtual manikin necessary to the realization of tasks in an industrial context. Great attention will be given to generated efforts to produce the movements and their adequacy with physical realism. The new method will be applied to industrial use cases and simulations using virtual reality.
Deadline : 2024-08-31
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(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M AI model audit
Rich and complex AI systems are increasingly used across multiple actors of society, from large language models to diagnostic models.
Stakeholders generally ask for these systems to be audited, with initiatives such as the AI safety institute and many questions from
regulatory bodies. However, there is a mismatch between the probabilistic objects of modern AI and the desired safety warrants: certitudes at the level of individual cases. From an engineering standpoint, in many complex settings, answers are best expressed with uncertainty quantification. From an audit standpoint, this quantification needs to be evaluated, both in its own right and insofar as it is linked to
decision-making. This control of uncertainty is recognized as one of the main challenges of machine learning in high-stakes applications such as healthcare [11].
One challenge lies in the fact that individual probabilities are never directly observed; instead, only discrete labels are available. The
machine-learning literature has predominantly focused on the concept of “calibration error” [5], which controls the error rate given a
confidence score (i.e. a probabilistic output). A calibration error of zero implies that a predictor is neither over-confident nor under-
confident. However, this measure being an average control applied across all individuals, it does not preclude the possibility of systematic over-confidence for some individuals and under-confidence for others. Likewise, conformal prediction methods come with certain guaranties on uncertainty, yet the strong results are marginal [9]. “Proper scoring rules” give finer a characterization: these functions fully control errors on individual probabilities via observed samples [4]. Yet, their value does not relate simply to an error rate that can be understood in application terms. Our recent research has delved into the decomposition of these into calibration, grouping, and irreducible errors. We have introduced an estimator for the grouping term, thereby completing existing estimators of calibration error. This approach allows for a comprehensive characterization of errors in probabilistic predictions [6]. Using these tools on large language models reveals cultural biases where the models’ uncertainty is more erroneous for answers about east-Asians than north Americans; biases that can then be partly corrected [2].
Deadline : 2024-08-31
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Statistical Learning on Flow Cytometry Data for the early characterization of Acute Myeloid Leukemia (IDP 2024)
Acute Myeloid Leukemia (AML) is an aggressive form of bone marrow cancer characterized by the proliferation of immature blood cells. The typical treatment is intensive chemotherapy that starts as early as possible. For some patients, this treatment turns out to be ineffective. Alternative treatment and/or inclusion in a clinical trial could be proposed if only these patients could be identified from the diagnosis.
A recent study (Itzykson et al., 2021) proposed a therapeutic decision tool based on cytogenetic and molecular biomarkers (chromosomal abnormalities, mutations). It is able to classify patients in three groups based on the adequacy of intensive chemotherapy (favorable, adverse or intermediate). Unfortunately, these biomarkers are obtained too late to inform the initial therapeutic decision.
In this PhD thesis, the goal is to develop statistical learning approaches for flow cytometry data obtained at diagnosis, in order to predict the cytogenetic and molecular prognosis markers for each patient.
The work is based on the collaboration with the team of Pierre-Yves DUMAS (PU-PH) at Bordeaux University Hospital Center, and implies the Regional Data Registry DATAML (Didi et al., 2024).
Deadline : 2024-08-31
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Stochastic modelling of oceanic flow, small-scale dynamics
The theoretical framework on which we rely, referred to as “modelling under location uncertainty”, decomposes the flow in terms of a resolved smooth component and a rapidly oscillating random component.The stochastic dynamics is then defined from a stochastic representation of the Reynolds transport theorem.From this modelling principle, stochastic equivalents of the classical geophysical flow models can be defined.
A set of models ranging from multi-layers quasi-geostrophic models to primitive equations have been in this way defined and numerically implemented. Ensemble data assimilation are currently under development as well as simplified ocean atmosphere coupled models.
The present PhD position aims to explore a variational formalism, recently proposed by A. Debussche and E. Mémin for the incompressible Euler equation, to infer a dynamics for the noise term, and more specifically, the correlation tensor involved in the definition of the small-scale component. The objective will be to extend this methodology to ocean dynamics and to study numerically and theoretically the corresponding system of equations. The second objective will be to explore more specifically such a system for wave-current interaction description. This post-doc will complement the numerical and theoretical efforts of the team on oceanic dynamics.
Deadline : 2024-08-31
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Type-based security properties assurance in operating systems
The PhD thesis is fully funded under the framework of a partnership between Inria and ANSSI. The PhD student will be supervised by researchers from Team SUSHI in collaboration with cyber-security experts from ANSSI.
Deadline : 2024-08-31
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Vehicle-and-mobile phone computing sharing as part of the edge-to-cloud continuum
Deadline : 2024-08-30
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Trustworthy AI hardware architectures
The direct consequence is the intense activity in designing custom and embedded Artificial Intelligence HardWare architectures (AI-HW) to support energy-intensive data movement, speed of computation, and large memory resources that AI requires to achieve its full potential. Moreover, explaining AI decisions, referred to as eXplainable AI (XAI), is highly desirable in order to increase the trust and transparency in AI, safely use AI in the context of critical applications, and further expand AI application areas. Nowadays, XAI has become an area of intense interest.
AI-HW, similar to traditional computing hardware, is subject to faults that can have several sources: variability in fabrication process parameters, latent defects or even environmental stress. One of the overlooked aspects is the role that HW faults can have in AI decisions. Indeed, there is a common belief that AI applications have an intrinsic high-level or resilience w.r.t. errors and noise. However, recent studies in the scientific literature have shown that AI-HW is not always immune to HW errors. This can jeopardize all the effort of having an explainable AI, leading any attempt to explainability to be either inconclusive or misleading. In other words, AI algorithms retain their accuracy and explainability property under the condition that the hardware wherein they are executed is fault-free.
Therefore, before explaining the decision of an AI algorithm – to gain confidence and trust in it – firstly the reliability of the hardware executing the AI algorithm needs to be guaranteed, even in the presence of hardware faults. In this way, trust and transparency of an implemented AI model can be ensured, not only in the context of mission- and safety-critical applications, but also in our everyday life.
Deadline : 2024-08-27
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Workflow Provenance and Its Application to Explainable and Transparent Artificial Intelligence
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 : 2024-08-24
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Data assimilation of satellite data in oceanic models, learning of oceanic dynamics
The Odyssey team is offering a PhD position on numerical ocean dynamics simulation, machine learning and data assimilation.
Odyssey (for Ocean DYnamicS obSErvation analYsis) is a recently created team involving researchers from Inria (Rennes, France), Ifremer (Brest) and IMT Atlantique (Brest).
Inria is one of the leading research institute in Computer Sciences in France, and Odyssey is also affiliated to the mathematics research institute of the Rennes University (IRMAR).
The team expertise encompasses mathematical (stochastic) and numerical modelling of ocean flows, observational and physical oceanography, data assimilation and machine learning.
Gathering this large panel of skills, the team aims at improving our understanding, reconstruction and forecasting of ocean dynamics, and more specifically to bridge model-driven and observation-driven paradigms to develop and learn novel representations of the coupled ocean-atmosphere dynamics ocean models.
Deadline : 2024-08-21
(28) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student M/F PhD – Detection and explanations of individual and collective behavior of individuals within a group to estimate their well-being
This thesis is part of the field of Artificial Intelligence, and more precisely of machine learning on time series with the objective of explaining the behavior of individuals.
A natural or human system such as a community, a team or even a herd of animals is generally composed of a set of individuals organized in groups governed by different types of social relationships ($ie$ interactions between individuals). The data available on these systems are mainly time series, data collected from many sensors and which measure different variables of the system at different time steps. The exploitation of these time series makes it possible to capture the dynamics of behaviors, essential for estimating or learning many characteristics about individuals within their group.
The context of this thesis is therefore interested in approaches to characterize the state of well-being of individuals by involving data at the individual level but also at the group level. The proposed approaches must be generic, however an application will be carried out within the framework of the PEPR WAIT4 project “Agroecology and digital technology”, in order to evaluate indicators of animal welfare faced with the challenges of the agro-ecological transition.
Deadline : 2024-08-19
(29) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Exploring low-precision arithmetic for continual learning tasks on edge devices
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 : 2024-08-15
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Robust and Agile Transportation of Cable Suspended-Loads with Multi-Drone Systems
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 : 2024-08-14
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Reliability Enhancement of Unconventional AI Accelerators
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 : 2024-08-10
(32) 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 : 2024-08-09
(33) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Improving security and performance of IPFS’s DHT
The PhD student will be hosted by Coast team. Collaborative work between COAST and RESIST teams and HIVE. Funding granted by France 2030, project PEPR Cloud, subproject TrustInCloudS.
Deadline : 2024-08-08
(34) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M LLM-based code generation for controlling artificial agents in simulated environments
Program synthesis (Chaudhuri et al., 2021) has been traditionally considered for different programming tasks, but very little studied for synthesizing controllers, i.e. programs controlling artificial agents in simulated environments. Such controllers are usually studied under the Reinforcement Learning (RL) paradigm in AI (Sutton & Barto, 2018), where an agent learns an action policy from experience in order to maximize cumulative reward in a simulated environment. We believe that the recent rise of Large Language Models (LLMs) opens important perspectives and novel directions for controller synthesis. The successes of Copilot and ChatGPT show that LLMs can provide help and assistance in many programming tasks, both saving time and reducing errors through for instance bug finding and code suggestions (Chen et al., 2021). Moreover, LLMs are increasingly used in RL, in particular in LLM-Augmented RL, where LLMs can interpret users’ intent and translate them into concrete rewards to be integrated in a RL algorithm (Fan et al., 2022). A few recent papers have proposed to use LLMs for generating AI controllers, reward functions and tasks (Faldor et al., 2024; Lehman et al., 2022; Wang et al., 2023), opening many exciting perspectives where the code generation abilities of LLMs is leveraged to synthesize AI agents and their environments.
In this project, we propose to explore novel research directions for using LLMs to synthesize controllers in unknown and complex environments. How to generate a controller program from a natural language description of the environment dynamics and task properties? Are LLM able to generalize to the generation of controllers that combine skills from previously discovered controllers? How to generate adaptive controllers, e.g. in the form of code specifying neural architectures using standard deep learning libraries such as Pytorch? Can LLM be used to generate the morphology of embodied agents? Can LLM help to disentangle semantic vs procedural knowledge in the context of controller synthesis? In particular, we believe that the question of combining skills is the key to scale these techniques to large environments and complex tasks.
Deadline : 2024-08-04
(35) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Reliable Deep Neural Network Hardware Accelerators
Deep Neural Networks (DNNs) [1] are currently one of the most intensively and widely used predictive models in the field of machine learning. DNNs have proven to give very good results for many complex tasks and applications, such as object recognition in images/videos, natural language processing, satellite image recognition, robotics, aerospace, smart healthcare, and autonomous driving. Nowadays, there is intense activity in designing custom Artificial Intelligence (AI) hardware accelerators to support the energy-hungry data movement, speed of computation, and memory resources that DNNs require to realize their full potential [2]. Furthermore, there is an incentive to migrate AI from the cloud into the edge devices, i.e., Internet-of-Things (IoTs) devices, in order to address data confidentiality issues and bandwidth limitations, given the ever- increasing internet-connected IoTs, and also to alleviate the communication latency, especially for real-time safety-critical decisions, e.g., in autonomous driving.
Hardware for AI (HW-AI), similar to traditional computing hardware, is subject to hardware faults (HW faults) that can have several sources: variations in fabrication process parameters, fabrication process defects, latent defects, i.e., defects undetectable at time-zero post-fabrication testing that manifest themselves later in the field of application, silicon ageing, e.g., time-dependent dielectric breakdown, or even environmental stress, such as heat, humidity, vibration, and Single Event Upsets (SEUs) stemming from ionization. All these HW faults can cause operational failures, potentially leading to important consequences, especially for safety-critical systems.
HW-AI comes with some inherent resilience to HW faults, similar to biological neural networks. Indeed, the statistical behavior of neural network architectures, as well as their high space redundancy and overprovisioning, naturally provide a certain tolerance to HW faults. HW-AI have the capability to circumvent to a large extent HW faults during the learning process. However, HW faults can still occur after training. Recent studies in the literature have shown that HW-AI is not always immune to such HW faults. Thus, inference can be significantly affected, leading to DNN prediction failures that are likely to lead to a detrimental effect on the application [3, 4, 5]. Therefore, ensuring the reliability of HW-AI platforms is crucial, especially when HW- AI is deployed in safety-critical and mission-critical applications, such as robotics, aerospace, smart healthcare, and autonomous driving.
Deadline : 2024-08-02
(36) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Responsible Reinforcement Learning: Robustness and Privacy in and by Sequential Decision Making
As RL algorithms are getting deployed in real-life the questions of responsible deployment, such as robustness to noise and perturbation to the feedback from environment, and privacy if users are involved in the environment yielding data.
Our works have shown that for structure-less and linear settings of multi-armed bandits and active testing (aka pure exploration) imposing privacy yields two regimes of performance. For the regime used in practice, privacy can be preserved without loss of utility. But our existing approach is not directly applicable to more practically appealing settings of RL, like MDPs or bandits with side-information (aka contexts). In these settings, there is a gap between achievable performances and the algorithms. Thus, we want to study whether the cost of privacy in contextual bandits and MDPs, and also to design optimal, computationally efficient algorithms.
Similarly, we have studied impact of unbounded corruption in feedback and safety constraints in stochastic multi-armed bandits and active testing (aka pure exploration). We want to understand how do they impact more structured RL problems and how can we design optimal algorithms in these setting.
The project is expected to simulate the existing and new collaborations with researchers and groups working on privacy-preserving machine learning, robustness, adaptive testing, and reinforcement learning. In future, the candidate will be encouraged to not only work with us but collaborate internationally. The candidate will also be part of the HumAIn alliance that aims toward studying humane impact of deploying AI.
Deadline : 2024-08-01
(37) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Modeling of large communication networks
The position is funded by the national program on communication networks
(PEPR réseaux du Futur, https://www.entreprises.gouv.fr/fr/actualites/france-2030-lancement-du-programme-reseaux-du-futur-et-de-france-6g).
The research will be conducted at INRIA and Télécom Paris in LINCS, a joint laboratory on communication networks.
Deadline : 2024-07-31
(38) 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 : 2024-07-31
(39) 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 : 2024-07-31
(40) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Doctoral Position on Lattice Dynamics & Phononic Metamaterials
Inria is the French national research institute for digital science and technology. This research center for scientific excellence is on the frontline of digital transformation in Europe and conducts a world-class research covering a wide range of high-impact scientific disciplines: international and industrial collaborations, ground-breaking research, software development, artificial intelligence (AI) and technological startups (DeepTech) are the DNA of the institute. Inria rank 16th worldwide at the AI Research ranking and is the number one European institute for frontier research in digital sciences.
Deadline : 2024-07-31
(41) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Foundation Models of human brain function
One of the major directions for future neuroscience is to build on the expertise accumulated in AI-powered
cognitive systems, such as architectures that process language or visual content, but in the future will also
include motor actions, planning and navigation. While both AI and neuroscience will benefit from comparing
brain activity data with AI systems, one difficulty is that the links between AI models and brain activity
have been made in very specific contexts [1, 2, 3] and may not generalise beyond a few standard situations
(static images, language, sounds).
A recent and beneficial shift in recent years has been the development and public sharing of large-scale brain
imaging datasets, whether performed on large populations [4, 5, 6] or on small groups of individuals but with
very large amounts of data available [7, 8, 9] and http://www.cneuromod.ca – a context known as deep
phenotyping. Given the availability of such data, which are only partially or inconsistently annotated [10],
the question is: can one identify core structures of these networks that would provide relevant primitives for
fitting AI models?
Deadline : 2024-07-31
(42) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Efficient Training of Neural Networks
The unprecedented availability of data, computation, and algorithms has enabled a new era in AI, as evidenced by breakthroughs like Transformers and LLMs, diffusion models, etc., leading to groundbreaking applications such as ChatGPT, generative AI, and AI for scientific research. However, all these applications share a common challenge: they keep getting bigger, which makes training models harder. This can be a bottleneck for the advancement of science, both at industry scale and for smaller research teams that may not have access to very large training infrastructure. While there already exists a series of effective techniques (e.g., see the overview [2]), recent ones either still rely on manual hyperparameter settings or lack automatic joint optimization of orthogonal approaches (e.g., pipelining and advanced re-materialization).
Deadline : 2024-07-31
(43) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Fine grain energy consumption measurement of HPC task-based programs
This thesis is placed in the context of NumPEx (https://numpex.fr/), a key national project whose goal is to co-design the software stack for the exascale era and prepare applications accordingly.
This thesis will be co-supervised by Inria Benagil (located in Evry) and Inria STORM (located in Bordeaux). Beyond the supervision, collaborations within NumPEx with the different partners of the consortium are to be expected.
Deadline :2024-07-28
(44) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Dimensioning probabilistic embedded systems for efficient execution of artificial intelligence algorithms
The PhD thesis is funded by the Paris region program and it is hosted by the Kopernic team in Paris (see more details at https://team.inria.fr/kopernic/ )
Supervised by Liliana Cucu-Grosjean (https://who.rocq.inria.fr/Liliana.Cucu/Welcome.html ), the student interacts with Kopernic members as well as with StatInf members, a Kopernic spin-off (https://statinf.fr). The thesis is expected to start as soon as possible and no later than December 1st, 2024.
Travelling is expected in France and in Bresil as well as EU countries, the associated costs being covered following the current public laws. Inria offers an equal opportunity and friendly working environnement, while covering partially the transport and meal costs. AGOS (its commité d’entreprise) provides financial support for holidays or jobbies.
Deadline : 2024-07-27
(45) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Incremental Deep Learning for Embedded Systems
This PhD will occur in the context of the project Adapting (https://www.pepr-ia.fr/en/projet/adapting-2/) from the PEPR AI (https://www.pepr-ia.fr/en/pepr/). This project focuses on designing adaptive embedded hardware architectures for AI. In this context, our team wants to design new incremental machine learning algorithms that could serve as use cases in the Adapting project for other researchers who will focus on the hardware architecture design. Continual learning , also known as lifelong learning or incremental learning, is a machine learning paradigm that focuses on the ability of a model to learn and adapt continuously over time as it encounters
new data, tasks, or concepts, without forgetting or catastrophically overwriting what it has previously learned. In continual/incremental learning, the learned model should retain knowledge about previous tasks or data while incorporating new information. In this PhD, we will focus on designing new resource-efficient incremental learning algorithms that can run on embedded systems with their associated ressource and privacy constraints. These contraints involve limited computational power, memory, and energy efficiency. They also involve real-time processing with low latency and often deterministic behavior. Updating embedded models is complex due to hardware limitations and the need for efficient updates while handling data locally to enhance privacy and security. This PhD will focus on foundation models such as well-known LLM -Large Language Models- (e.g. GPT-3.5, Mixtral, Llama 3,…) or multimodal ones (involving for example ViT -Vision Transformer- models such as GPT-4o, Sora, Dall-E 3) and their ability to evolve continuously in an embedded environnement.
Deadline : 2024-07-26
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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