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 Online Learning with Limited Resources
Online learning algorithms [Hazan22,Shalev12] have shown substantial promise across various future networks’ applications, including caching [Bhattacharjee20,Paschos19,SiSalem23], resource allocation in radio access networks [Kalntis24], and machine learning model placement [SiSalem24].
This thesis focuses on advancing online learning algorithms that offer theoretical guarantees against an adversary who selects the sequence of inputs with the goal to jeopardize system performance. Such adversarially robust algorithms are particularly beneficial for scenarios characterized by highly dynamic user demands and/or rapidly evolving network conditions.
A key metric in evaluating the robustness of these algorithms is regret, which measures the largest discrepancy between the algorithm’s experienced cost and that of the optimal static policy in hindsight (i.e., one that has prior knowledge of the entire input sequence). The objective is to develop algorithms with sublinear regret growth relative to input sequence length, ensuring that their per-input-average cost asymptotically approaches that of the best static policy.
Deadline : 2025-03-31
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Scalable Translation Validation for High-Performance Computing and Machine Learning
The overall objective of this PhD thesis is to investigate translation validation of programs with reductions and compile-time data allocation. In particular, the PhD student will address the following points.
- Verifying reductions. Many reduction transformation exist (factorization, semantic tiling, reduction parallelization). How to formalize them in unified way? How to support the composition with loop transformations? How that formalization might be produced by the compiler? Finally, how to check it in a scalable way? The polyhedral model provides a formalization of some of these transformations which enables solver-based checking. A reduction-compliant extension could be investigated.
- Verifying data allocation. The same questions arise for compile-time data allocation required by automatic parallelization (array privatisation, array contraction, struct/array permutation, etc) and will be investigated as well. In particular the framework of linear intra-array allocation and affine inter-array allocation could help to find a relevant formulation.
- Scalability. If possible, a first direct solver approach will be proposed for simple cases. Then, the scalability will be addressed to handle real-life HPC programs. How to parallelize the whole process? How to reduce the overall complexity? A trace-based solution could also be investigated.
- Validation. The approach will be validated on HPC benchmarks.
Deadline : 2025-03-31
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(03) 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 : 2025-02-28
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Robust Federated Learning
Federated Learning (FL) empowers a multitude of IoT devices, including mobile phones and sensors, to collaboratively train a global machine learning model while retaining their data locally [1,2]. A prominent example of FL in action is Google’s Gboard, which uses a FL-trained model to predict subsequent user inputs on smartphones [3].
Two primary challenges arise during the training phase of FL [4]:
Data Privacy: Ensuring user data remains confidential. Even though the data is kept locally by the devices, it has been shown that an honest-but-curious server can still reconstruct data samples [5,6], sensitive attributes [7,8], and the local model [9] of a targeted device. In addition, the server can conduct membership inference attacks [10] to identify whether a data sample is involved in the training or source inference attacks to determine which device stores a given data sample [11].
Security Against Malicious Participants: Ensuring the learning process is not derailed by harmful actors. Recent research has demonstrated that, in the absence of protective measures, a malicious agent can deteriorate the model performance by simply flipping the labels [12] and/or the sign of the gradient [13] and even inject backdoors into the model [14] (backdoors are hidden vulnerabilities, which can be exploited under certain conditions predefined by the attacker, like some specific inputs).
Deadline : 2025-01-31
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Reliability Enhancement of Post-Von Neumann Hardware Accelerators
The Ph.D. student will characterize the radiation-induced impact on system reliability for different DNN model architectures and how the acceleration that PIM enables impacts the final error rate. The results will be combined with software simulation data for a detailed fault propagation analysis, aiming at deploying effective hardening solutions tailored for PIM executing DNNs.
The Ph.D. student will participate in international experiments and internships at laboratories like Rutherford Appleton Laboratory in the UK and Los Alamos National Laboratory in the USA. The student will participate in conferences and international projects and have their research published in prestigious scientific venues. This will help them develop their research skills and network with professionals in their field.
Deadline : 2025-01-15
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M LLM4Code and SopraSteria: Software migration and modernization with LLMs
The candidate will be employee of SopraSteria and spend part of the time in SopraSteria and in DiverSE.
The work is also part of a Défi Inria LLM4Code “Reliable and productive Code Assistants based on large language models” with more than 10 research teams working on several aspects of LLMs and code. Hence the candidate will have the opportunity to collaborate with numerous researchers and experts, as well as to leverage computational infrastructure and the SoftwareHeritage project.
Deadline : 2025-01-15
(07) 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
(08) 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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(09) PhD Degree – Fully Funded
PhD position summary/title: Doctorant F/H Optimisation et algorithmes de contrôle pour les agents distribués dans les réseaux d’énergie
Les travaux de cette thèse visent des résultats théoriques qui seront présentés dans des conférences et des journaux dans l’apprentissage (e.g. ICML, NeurIPS), contrôle (e.g. IEEE CDC, IEEE Transactions on Automatic Control), ou visant des applications en énergie (e.g. IEEE Transactions on Smart Grids).
Deadline : 2024-12-31
(10) 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 : 2024-12-31
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(11) 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 : 2024-12-24
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Experimentation with LLMs for Fortran migration
During the project the phd student will focus on assessing the possibility of performing a software migration with LLMs in the specific context of a given niche technology for a given organization (specific domain, specific development culture).
Deadline : 2024-12-24
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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