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 Stochastic modelling of dynamical resource allocation and analysis of single-cell data
The Ph.D. project will be carried out in the project-team MICROCOSME at Inria Grenoble – Rhône-Alpes under the joint supervision of Aline Marguet (https://team.inria.fr/microcosme/aline-marguet/) and Hidde de Jong (https://team.inria.fr/microcosme/hidde-de-jong/) within the framework of the ARBOREAL ANR project (https://project.inria.fr/arboreal/). MICROCOSME is an interdisciplinary team that includes applied mathematicians, engineers, computer scientists, biologists as well as experimentalists from the microbiology/biophysics team BIOP of the Université Grenoble-Alpes (https://liphy.univ-grenoble-alpes.fr/fr/recherche/equipes/biop-fluctuations-regulations-et-evolution-systemes-vivants).
Deadline : 2025-09-30
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Formal Verification of Higher-Order, Probabilistic Programs
This PhD thesis project is part of the ANR project HOPR (Higher-Order Probabilistic and resource-aware Reasoning) (ANR-24-CE48-5521-01) coordinated by P. Baillot, starting in 2025 and aiming at defining expressive logical frameworks, dealing in particular with higher-order computation and probabilities, which can serve to reason on cryptographic primitives and protocols and on differential privacy. The project has three partner sites: INRIA Lille/CRIStAL; INRIA Paris; IRISA Rennes and INRIA Sophia-Antipolis. It is starting in January 2025 for 4 years.
The recruited PhD student will carry out her/his research within the SPLITS and OLAS project-teams at INRIA Sophia Antipolis, under the supervision of B. Gregoire and M. Avanzini
Deadline : 2025-07-31
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(03) 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 : 2025-06-30
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Machine Learning based Program Recognition
The overall objective is to design a static analysis able to recognize automatically a program by leveraging machine learning; and its application to automatic program optimization. The research includes the implementation of the solution and the experimental validation required for the related publications.
Deadline : 2025-06-30
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Robust Federated Learning
The position is part of a new Marie Curie Training Network called FINALITY, in which Inria joins forces with top universities and industries, including IMDEA, KTH, TU Delft, the University of Avignon (Project Leader), the Cyprus Institute, Nokia, Telefonica, Ericsson, Orange, and others. The PhD students will have opportunities for internships with other academic and industry partners and will be able to participate in thematic summer schools and workshops organized by the project.
Only people who have spent less than one year in France in the last 3 years are eligible.
The candidate will receive a monthly living allowance of about €2,735, a mobility allowance of €414, and, if applicable, a family allowance of €458 (gross amounts).
Deadline : 2025-05-31
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Machine Learning Trustability : Learning and Verification of Soft Automata
The objective of this PhD is to explore the way various NN-based architectures manage to approximate formal languages, i.e. learn surrogate automata from their traces. Beyond well established results on the expressive power of these models, the focus will be on the capabilities of the pair model + learning algorithm. Several authors have shown that almost discrete behaviors emerge naturally when NN are trained by automata traces, despite their definition as continuous state space systems, whence the name “soft automata.” Another objective will be to assess the robustness and reliability of such NN-based models as automata approximators, by means of appropriate formal methods.
Deadline : 2025-05-31
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD position on Verification of Differential Privacy
This PhD thesis project is part of the ANR project HOPR (Higher-Order Probabilistic and resource-aware Reasoning) (ANR-24-CE48-5521-01) coordinated by P. Baillot, starting in 2025 and aiming at defining expressive logical frameworks, dealing in particular with higher-order computation and probabilities, which can serve to reason on cryptographic primitives and protocols and on differential privacy. The project has three partner sites: INRIA Lille/CRIStAL; INRIA Paris; IRISA Rennes and INRIA Sophia-Antipolis. It is starting in January 2025 for 4 years.
Deadline : 2025-05-14
(08) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H LLM4Code : Coévolution continue du code pour les langages et bibliothèques grand public (LLM4Code : Continuous code co-evolution for mainstream languages and libraries)
La mission de cette thèse s’articule principalement autour de la réalisation d’une recherche d’excellence, que l’équipe DiverSE s’efforce de mener.
Un état de l’art fera partie des premières activités afin de mieux préparer le terrain à l’implémentation de solutions et de prototypes, ainsi qu’à la réalisation d’expériences empiriques pour une évaluation rigoureuse des contributions.
Deadline : 2025-05-04
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Topology Design for Decentralized Federated Learning
The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL) [li20,kairouz21], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [mcmahan17] allow clients to train a common global model without sharing their personal data. FL reduces data collection costs and can help to mitigate data privacy issues, making it possible to train models on large datasets that would otherwise be inaccessible. FL is currently used by many big tech companies (e.g., Google, Apple, Facebook) for learning on their users’ data, but the research community envisions also promising applications to learning across large data-silos, like hospitals that cannot share their patients’ data [rieke20].
In the classic FL setting, a server coordinates the training phase. At each training round, the server sends the current model to the clients, which individually train on their local datasets and send model updates to the server, which in turn aggregates them (often through a simple averaging operation). In contrast to this client-server approach, decentralized FL algorithms (also called P2P FL algorithms) work by having each client communicate directly with a subset of the clients (its neighbours): this process alternates between model updates and weighted averaging of the neighbours’ models (consensus-based optimization). Decentralized algorithms can take advantage of good pairwise connectivity, avoid the potential communication bottleneck at the server [marfoq20] as well as provide better privacy guarantees [cyffers22].
Deadline : 2025-04-30
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Reliability Enhancement of Post-Von Neumann Hardware Accelerators
Artificial Intelligence (AI) is increasingly indispensable across various society sectors due to its potential to transform conventional applications, from smart homes to safety-critical systems like autonomous driving and space exploration. Deep neural networks (DNNs) are state-of-the-art AI methods that outperform other approaches in language processing, image and video classification, audio and radar processing, and instance segmentation [1–3]. Notably, DNNs such as OpenAI GPT-4, Meta LLaMA2, and Mistral Mixture of Experts have captivated public interest with their high accuracy.
Due to their resource-intensive nature, DNNs require powerful dedicated hardware accelerators, such as GPUs and TPUs. However, large hardware accelerators are unsuitable for embedded safety-critical systems due to their high energy consumption. New unconventional accelerator architectures like the ones based on PIM [4] and neuromorphic computing [5] have been proposed for complex DNN deployment in critical applications where power and performance are critical requirements, offering energy-efficient alternatives to traditional GPUs and TPUs. However, their reliability, particularly against radiation-induced faults, remains to be fully assessed.
Deadline : 2025-04-30
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Knowdgets: Widgets Supporting Knowledge of Interaction
This Ph.D. is funded by the Knowdgets project, which aims to redefine widgets, in what we call Knowdgets, to address the limitations of current widgets and to propose new programming approaches. The Ph.D. will be conducted in the Loki team at the University of Lille and the CRIStAL laboratory, in collaboration with the LII team at ENAC in Toulouse.
Widgets (buttons, sliders, etc.) are the building units available in toolkits to create user interfaces. They are designed to interpret users’ actions (e.g. click on a button), change their graphical representation to represent their internal state (e.g. button pressed) and translate the actions into operations in an application.
As such, graphical toolkits have made it convenient for developers to assemble interfaces from pre-defined widgets, and for users to recognize these components and their behaviors. However, this convenience comes at the cost of having pre-defined widgets that constrain and limit both the interaction vocabulary the interface can support and its extensibility.
Indeed, current widgets typically support a limited set of user actions (e.g. tap and long press on a button). As a result, beyond forms, data entry and command selection, it quickly becomes necessary for developers to create custom widgets, thus giving up on the toolkit’s benefits, or worse, having to bypass its limitations.
Deadline : 2025-04-30
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(12) 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.
Deadline : 2025-04-30
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Rare-event detection with local pattern modeling for large scale physical simulations
This PhD focuses on developing novel unsupervised machine learning techniques for rare-event detection in large-scale physics simulations on high-performance computing (HPC) clusters. By leveraging in situ processing, the goal is to efficiently characterize local data distributions, identifying rare but meaningful events and anomalies while minimizing computational and communication overhead. The project will explore convolutional dictionary learning and hybrid unrolled models to enhance interpretability and scalability, with a strong emphasis on benchmarking and integration into real-world scientific applications. The research aims to contribute to machine learning for science by improving event detection methodologies and fostering interdisciplinary collaborations.
Deadline : 2025-04-30
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M [Campagne Allocation Région 2025] Robotization of Cochlear Implant Insertion Surgery: Modeling, Simulation, and Control (F/H)
According to the statistic of World Health Organization, over 5% of the world’s population, i.e., 360 million people, has disabling hearing loss (328 million adults and 32 million children). Cochlear implant surgery can be used for profoundly deafened patient, for whom hearing aids are not satisfactory, and it is regarded as one of the best options for better hearing. During the implant surgery, the most difficult task is to insert the electrode array into the tympanic ramp of the patient’s cochlea. The implant is normally made of silicone (thus very soft), the surgery is performed manually because the cochlear implant is totally passive and the surgeon has no perception on what happens in the cochlea while he/she is doing the insertion.
This thesis aims to significantly advance the automation of cochlear implant insertion, progressing from TRL 3 to TRL 6. It is partially funded by the ANR PRCE project ACCESS and seeks to address critical challenges in the modeling, simulation, and control of active Thin-Film Electroactive Actuators (TFEAs) for cochlear implantation. A primary focus of the research is to develop robust solutions for navigating the complex anatomy of the cochlea and its surrounding deformable structures, which present significant challenges for both the design of the implant and the precision of its insertion.
Deadline : 2025-04-20
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M [Campagne Allocation Région 2025] Automatic Generation of Attack Chains for Detecting and Preventing Software Vulnerability (F/M)
The recruited person will be taken to: (1) develop a modular approach to vulnerability analysis, (2) build a tool dedicated to the automatic generation of attack chains via fuzzing and mutation and (3) study the history and semantics of code changes for the understanding of attacks. Prototypes will be developed in the Pharo language.
Deadline : 2025-04-20
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M [Allocation Région 2025] Towards quantum-utility multi-objective variational optimisers (F/M)
Quantum Computing paradigm (QC) is based on quantum mechanical principles, which allows it to provide computation acceleration regarding its classical counterpart. Optimisation problem-solving is one of the major domains where QC can provide advances. Variational Quantum Algorithms (VQAs) are a promising class of quantum optimisers capable of providing promising efficiency despite the noisy-limited nature of today’s quantum devices. Initially, VQAs have been designed to solve single-objective problems, although, real-life scenarios require dealing with multiple ones. Thus, extending this class of algorithms to MO domain is challenging and paramount towards realistic applicability and eventually attaining a possible quantum advantage. So far, only some efforts have been made to design multi-objective counterparts of VQAs (MO-VQAs). In addition, the limited literature that attempted to do so presents shortfalls preventing the applicability and efficiency of such proposals.
This thesis aims to (I) correct shortfalls of previous research, and (II) push the boundaries of research on MO-VQAs beyond current state-of-the-art by exploring never-research-before approaches. The main challenge in both cases is to design utile MO-VQAs that are feasible, error-robust and efficient considering the noisy and limited nature of today’s quantum hardware.
Deadline : 2025-04-20
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M [Allocation Région 2025] Game Theory for Energy System Decarbonization (F/M)
The development of renewable energy production (solar, wind) defines new challenges in the management of energy production and transport systems, as well as in the functioning of electricity markets.
Currently, these markets are operated centrally by a market operator, whose objective is to maximize the social welfare (or equivalently, minimize the social cost) of participants, while ensuring the balancing of supply and demand. The connection between producers and consumers is done via an auction system.
The aim of this thesis is to define and implement computational game theoretic approaches making use of artificial intelligence for the management of electricity markets. At the scientific level, the integration of pollution constraints and learning models into “multi-leader single follower” optimization models represents a major challenge. At the application level, this tool could be used as a prototype to accelerate the decarbonization of the electricity system.
Deadline : 2025-04-20
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Forming carbon fiber fabrics by visual servoing
The main objective of this PhD thesis is to develop a robotic control approach to manipulate a flexible planar object (a carbon fiber fabric) to apply a desired shape curvature to it. The general idea is to use visual feedback provided by a camera to estimate and track in real-time the deformations of the object of interest and to develop a visual servoing control approach to control multiple robotic manipulators to autonomously apply a desired deformation to the object. This active deformation control would enable the implementation of new robotic applications such as automatic forming of a fiber fabric, which is particularly targeted in this thesis.
Deadline : 2025-04-18
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Resource-Aware Conservative Static Analysis
The PhD student will be part of the SyCoMoRES team of Inria Lille & CRIStAL lab, which currently hosts 4 fellow PhD students and one postdoc. Lille is a city close to Brussels, Paris & London, easily reachable by train, with a large student population and a number of cultural places & events. The lab has a very active equality and parity commission, which raises awareness on this topic to all staff (with specific events for newcomers), and provides outreach activities for high-schoolers. One of the advisors (Raphaël Monat) is an active member of this commission.
PhD students are appointed for a duration of 3 years. We plan to organize weekly research meetings with the PhD student. In addition, the student will be able to attend monthly meetings with other Mopsa practitioners. This research project is part of ANR JCJC RAISIN. We will hold quarterly project meetings with Sophie Cerf (member of the project), who is a researcher at Inria with expertise in control theory for software systems
Deadline :2025-04-14
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(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Knowledge Graph-Based Provenance Modeling for the Evaluation of Interactive Visualization Tools
This PhD subject is part of a doctoral grant awarded by the Université Côte d’Azur following a selection process. The start date is October 1, but can be flexible up to one month. More details on the selection process can be found on
https://webusers.i3s.unice.fr/edstic/3-2-candidater-en.php and for EUR-DS4H https://ds4h.univ-cotedazur.eu/education/phd
This thesis aims to advance analytical provenance in visualization tools by developing a structured model based on the Semantic Web to systematically capture and represent provenance data. It also proposes an evaluation framework to assess the usability and effectiveness of visualization techniques using this data. Finally, an extensible solution will be designed to seamlessly integrate provenance tracking into widely used web-based visualization libraries like D3.js, without requiring major modifications to existing systems.
Deadline : 2025-04-10
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M xOS: The End Of The Process-Thread Duo Reign
The Centre Inria de l’Université de Grenoble groups together almost 600 people in 23 research teams and 9 research support departments.
Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area.
The Centre Inria de l’Université Grenoble Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.
Deadline : 2025-04-06
(22) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Lighting estimation from images for seamless integration of virtual objects into real scenes
The objective of this thesis is to work on new techniques for estimating the position, orientation and intensity of light sources from videos, to seamlessly integrate virtual objects into real scenes.
We propose here to build on this work by first improving the current limitations and then generalizing the approach. The first step is to be able to work from videos where the lighting is dynamic. Indeed, the current approach only allows static lighting as we use a single image to compute the lighting. The key challenge lays in the ability to generate temporally stable light estimations over the sequence. A second step is to design a validation framework to assess the preciseness of the light reconstruction and the quality of the generated results, typically by using the ground truth generated by a game engine such as Unreal Engine (UE). More precisely, we would use a photo realistic scene and use not only the real video created by UE but also the 3D model and the HDR representation of the scene to compare our results to the same simulation in UE. Additional extensions would include the possibility to have a multi-pass rendering approach where the synthetic objects illumination also impacts the real scenes (consider for example the color bleeding of a virtual red sphere over a real white wall).
Deadline : 2025-04-04
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Online Learning with Limited Resources
The position is part of a new Marie Curie Training Network called FINALITY, in which Inria joins forces with top universities and industries, including IMDEA, KTH, TU Delft, the University of Avignon (Project Leader), the Cyprus Institute, Nokia, Telefonica, Ericsson, Orange, and others. The PhD students will have opportunities for internships with other academic and industry partners and will be able to participate in thematic summer schools and workshops organized by the project.
Deadline : 2025-03-31
(24) 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 : 2025-03-31
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Computational Bayesian optimal sensor placement for ocean models: a majorize-then-optimize strategy
The objective of this project is to address various numerical aspects associated with the gradient-based solution for the BOED problem. The project has three main goals:
- Firstly, we seek to enhance our understanding of the majorize-then-minimize approach used in the gradient-based solution. We will achieve this by comparing the solutions obtained from the bound-based approach with those obtained from the conventional EIG-based approach. Ultimately, we hope to use the bound-based approach as a preconditioning step for the EIG-based solution to improve its accuracy.
- Secondly, we will employ randomized linear algebra methods to accelerate the computation of the bound which, for realistic models, can still be quite expensive to compute. This will help to improve the computational efficiency of the gradient-based approach, making it more practical for large-scale systems.
- Finally, we will address the challenge of incorporating physical constraints into the sensor placement problem. Specifically, we will investigate how to take into account the constraints (physical/technical/financial) on the way the system can be observed, in order to obtain more realistic and practical sensor placement solutions.
Deadline : 2025-03-31
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD – Designing next-generation seismic metamaterials with hybrid particle- and wave-based simulations
Inria is the National Research Institute for Digital Science and Technology. This center for scientific excellence is directing the French Digital Programs Agency and is on the frontline of digitalization in Europe while conducting world-class research covering a wide range of disciplines. International and industrial collaborations, ground-breaking research, software development, artificial intelligence, quantum- and cyber technologies (AI) and deep tech startups are the DNA of the institute. Inria ranks 16th worldwide at the AI Research ranking while being the number one European institute for frontier research in digital sciences.
Deadline : 2025-03-31
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M FPGA-based Near Memory Computing Architectures
Moore’s law has been driving computer performance for decades through CMOS down-scaling and architecture enhancements, resulting in doubled performance every 18 months. However, current technology encounters three significant challenges, including the leakage wall, reliability wall, and cost wall. Similarly, computer architectures are confronted with three walls: the memory wall, power wall, and instruction-level parallelism (ILP) wall. Numerous novel technologies and architectures are being researched to overcome these walls and enhance performance [1], [2].
Architects and designers are compelled to seek breakthroughs in computer architecture as the total computation costs are dominated by the energy and performance costs of moving data between the memory subsystem and the CPU. Indeed, modern computing systems experience a significant disparity between the performance and energy efficiency of computation and memory units. Such systems adopt a processor-centric method where data must travel to and from memory units through a relatively slow and power-intensive off-chip bus to computation units for processing. Consequently, workloads that heavily rely on data necessitate constant data movement between memory and CPU, leading to a substantial overhead in execution time and energy efficiency [3].
Deadline : 2025-03-31
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Using AI as Design Material: Exploring the potential of GenAI for Design Practice (M/F)
This Ph.D. focuses on visual designers and explores how to design effective human-computer partnership to take advantage of GenAI in their professional practice while leaving the user in control. It builds on principles such as instrumental interaction [1] and co-adaptation [5] to create interactive systems that are discoverable [2], appropriable [4], and expressive [6], that grow with the user to enhance rather than replace the users skills.
Designers are a particularly demanding audience, who generate ideas and new artifacts for existing challenges. Advancements in AI, especially GenAI, hold large potential for design and artistic practice [7]. However, today’s (professional) AI tools exhibit a limited degree of human control or fine-tuning, which the creative process requires. Existing text prompt interactions limit GenAI’s usefulness in practice, particularly in design, where concepts are frequently represented visually, such as through sketches, moodboards, or prototypes. Enabling a more collaborative human-computer process requires that systems adapt their agency and interaction style throughout the creative process in a more situated way.
Deadline : 2025-03-30
(29) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Designing for Explainability in Sustainable AI (M/F)
Background. Recent generational leaps in the complexity and capabilities of Machine Learning (ML) models have made Artificial Intelligence (AI) able to tackle challenges ranging from vision and graphics to natural language, and even creative tasks. These improvements, along with the growing availability and maturity of AI technologies, also helped democratizing AI as a tool for a broad audience of researchers, industries, artists, and more. However this expansion also revealed the environmental and economic impacts of AI technologies when used at very large scales [3, 7]. The adoption of greener, less energy-consuming models by ML practitioners is a significant aspect in successfully improving AI impact in the future. However, there can exist hundreds of candidate algorithms to address a single category of problems, and the choice of a ML model for a given task is o en driven by previous experience, domain understanding, or expertise availability. Adopting new technologies and approaches typically requires additional learning efforts in order to fully understand their purpose, strength, features, and adequacy to a task.
Deadline : 2025-03-30
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Trustworthy AI hardware architectures
The goal of the Ph.D. thesis is to study the impact of hardware faults not only on the AI decisions, but also on algorithms developed to explain AI (XAI) models. The objective is to make AI-HW reliable by understanding how hardware faults (due to variability, aging, external perturbations) can impact AI and XAI decisions and how to mitigate those impacts efficiently. The final goal is to enable the transparency of the AI-HW by designing self-explainable, trustworthy, reliable, and real-time verifiable AI hardware accelerators, capable of performing self-test, self-diagnosis, and self-correction.
Deadline : 2025-03-26
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Language and speaker independent generic articulatory model of the vocal tract
This project aims to synthesize the temporal evolution of the vocal tract for any language and any speaker. It falls within the field of articulatory synthesis, seeking to model and simulate the physical process of human speech production via advanced approaches.
The work will make use of real-time MRI databases [3], which provide images of the evolution of the geometric shape of the vocal tract in the medio-sagittal plane at a frequency of 50 Hz. This frequency is sufficient to capture articulator gestures during speech production. We have data for around twenty speakers in several languages with different articulation points.
The task will be to build a dynamic model of the vocal tract that can be adapted to a specific language and speaker from these data.
Deadline : 2025-03-26
(32) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Speaker-independent acoustic to articulatory inversion of the entire vocal tract based on rt-MRI
The aim now is to develop a multi-speaker inversion of the complete vocal tract. To this end, we have data concerning about twenty speakers. These data are less complete than those used for the single-speaker inversion, but they will enable us to develop an anatomical normalization procedure to adapt the inversion to a new speaker, and to perform an acoustic adaptation.
Deadline : 2025-03-26
(33) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Real-Time execution of AI algorithms on embedded systems with partitioned memory
The Inria University of Lille centre, created in 2008, employs 360 people including 305 scientists in 15 research teams. Recognised for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria University of Lille centre pursues a close relationship with large companies and SMEs. By promoting synergies between researchers and industrialists, Inria participates in the transfer of skills and expertise in digital technologies and provides access to the best European and international research for the benefit of innovation and companies, particularly in the region.For more than 10 years, the Inria University of Lille centre has been located at the heart of Lille’s university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on Avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT).
Deadline : 2025-03-24
(34) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Design and analysis of parametric adaptive real-time systems
A real-time system controls a physical device in its environment, at a rate adapted to the device evolution. This requires not only to compute correct values, but also to compute values at the right time. Real-time systems can be found in several industrial domains, such as automotive, aeronautics, nuclear plants or automated production lines.
A real-time system is usually represented as a set of concurrent tasks subject to timing constraints (deadlines). In order to guarantee the respect of timing constraints, first a worst-case execution time (WCET) analysis is performed. Then, this information is used to perform a schedulability analysis so as to guarantee that all tasks will meet their deadlines, when executed concurrently at run-time.
Deadline :2025-03-21
About The National Institute for Research in Computer Science and Automation (Inria), France –Official Website
The National Institute for Research in Computer Science and Automation (Inria) is a French national research institution focusing on computer science and applied mathematics. It was created under the name Institut de recherche en informatique et en automatique (IRIA) in 1967 at Rocquencourt near Paris, part of Plan Calcul. Its first site was the historical premises of SHAPE (central command of NATO military forces), which is still used as Inria’s main headquarters. In 1980, IRIA became INRIA. Since 2011, it has been styled Inria.
Inria is a Public Scientific and Technical Research Establishment (EPST) under the double supervision of the French Ministry of National Education, Advanced Instruction and Research and the Ministry of Economy, Finance and Industry.
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