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15 PhD Programs – Fully Funded at National Institute for Research in Digital Science and Technology, France

Do you have a Master’s degree and are looking for fully funded PhD opportunities? National Institute for Research in Digital Science and Technology, France is now accepting applications for several funded PhD programs across a range of research areas.

1. Fully Funded PhD in F/M Auditing online AI models

Summary of Funded PhD Program

The widespread use of black-box machine learning models in decision-making systems has created a need for transparency and accountability. However, existing regulations and au-diting techniques are insufficient to address the challenges posed by these opaque models. These challenges include the need for efficient auditing (e.g. the computational tractability of these audits), and the ability to handle continuously evolving models. While active au- diting techniques show promise [3], they are limited to low-capacity models [1]. Thus, there is a need for more efficient and scalable auditing techniques to address the challenges posed by these modern black-box models. In addition, the auditing of remote online platforms’ models presents further challenges due to their constant evolution. Auditing algorithms that can handle dynamic models and provide practical guidance for regulators are dearly missing.

Application Deadline: 2024-11-30

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2. Fully Funded PhD in Online Learning with Limited Resources

Summary of Funded PhD Program

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.

Application Deadline: 2024-11-30

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3. Fully Funded PhD in Design and Implementation of a Scalable Naming Service in Shared Memory

Summary of Funded PhD Program

In this project, we aim to reimagine the architecture of cloud applications in the CXL era. Our approach decouples memory from processes, enabling global memory sharing across processes, similar to how threads share memory in multi-threaded applications. However, unlike the multi-threaded model, memory objects in this design can persist beyond the lifespan of individual processes, acting as long-term storage for ephemeral processes that are launched on demand to serve clients or handle large-scale data analytics. Since any process can directly access the global memory, the architecture avoids the high cost of transforming data when it is exchanged between the processes.

Application Deadline: 2024-11-30

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4. Fully Funded PhD in Scalable Translation Validation for High-Performance Computing and Machine Learning

Summary of Funded PhD Program

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).

Application Deadline: 2024-11-30

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5. Fully Funded PhD in Building physics-based multilevel surrogate models from neural networks

Summary of Funded PhD Program

Numerical simulations of electromagnetic wave propagation problems primarily rely on a space discretization of the system of Maxwell’s equations using methods such as finite differences or finite elements. For complex and realistic three-dimensional situations, such a process can be computationally prohibitive, especially when the end goal consists in many-query analyses (e.g., optimization design and uncertainty quantification). Therefore, developing cost-effective surrogate models is of great practical significance.

Application Deadline: 2024-11-30

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6. Fully Funded PhD in Higher-order interactions for brain-computer interfaces

Summary of Funded PhD Program

This PhD project aims to explore the role of higher-order interactions in the development of advanced brain-computer interfaces (BCIs). Current BCI systems often rely on linear models and first-order relationships to decode brain signals, which often leads to limitations in accuracy and adaptability. This research seeks to move beyond these constraints by investigating how higher-order interactions among neural signals can improve the performance and functionality of BCIs.

Application Deadline: 2024-12-01

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7. Fully Funded PhD in Multilingual and cross-cultural automatic analysis of argumentation structures in political debates

Summary of Funded PhD Program

In particular, this Ph.D. thesis aims to detect formal and informal logical fallacies in a multilingual corpus of political debates by integrating both argumentation theory and NLP into the detection process. Formal fallacies involve faulty logical reasoning, whereas informal fallacies may have a valid structure or form, but the premises are either untrue or irrelevant. This part of the thesis will expand on some already existing works that already focused on providing a taxonomy of formal and informal fallacies on various corpora, including political debates. 

Application Deadline: 2024-12-06

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8. Fully Funded PhD in Multilingual speech synthesis, with application to regional languages

Summary of Funded PhD Program

This PhD is part of the Inria COLaF Challenge “Corpora and Tools for the Languages of France”, which aims to create open, inclusive corpora, models and software for the languages of France. These include regional languages (Alsatian, Breton, Corsican, Occitan, Picard, etc.), overseas languages (Creoles, Polynesian, Kanak, Mahorese languages, etc.), and non-territorial immigrant languages (dialectal Arabic, Western Armenian, Berber, Judeo-Spanish, Romani, Yiddish).

Application Deadline: 2024-12-06

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9. Fully Funded PhD in Steering formal reasoning problems generation for LLM reasoning improvement

Summary of Funded PhD Program

Large Language Models (LLMs) are trained to predict missing words in many situations, which leads them to absorb knowledge, natural language structure, and some (brittle) algorithmic problem-resolution capabilities. By contrast, symbolic AI matured efficient algorithms to reliably solve various narrow problems (first order logic, modal logics, planning, constraint satisfation…), but it is challenging to successfully apply them in real world problems requiring natural language understanding and knowledge that is hard to formalize.

Application Deadline: 2024-12-11

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10. Fully Funded PhD in Trustworthy AI hardware architectures

Summary of Funded PhD Program

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

Application Deadline: 2024-12-24

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11. Fully Funded PhD in Topology Design for Decentralized Federated Learning

Summary of Funded PhD Program

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].

Application Deadline: 2024-12-31

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12. Fully Funded PhD in Optimization and control algorithms for distributed agents in energy networks

Summary of Funded PhD Program

The objective is to develop techniques and algorithms for optimization and distributed control for energy networks. In particular, we will study reinforcement learning algorithms based on partial knowledge of the system. An important question that we wish to address in this thesis is how to efficiently exploit the dependency structure between the different agents in the system. We plan to propose convergence guarantees for multi-agent reinforcement learning algorithms in the case of weak dependencies between agents (eg acyclic dependencies or with other properties facilitating the convergence analysis). 

Application Deadline: 2024-12-31

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13. Fully Funded PhD in privacy-preserving federate learning with applications in oncology

Summary of Funded PhD Program

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.

Application Deadline: 2024-12-31

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14. Fully Funded PhD in Deep Neural Network-assisted computational design of highly efficient ultrafast dynamical metasurfaces

Summary of Funded PhD Program

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.

Application Deadline: 2024-12-31

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15. Fully Funded PhD in Robust Federated Learning

Summary of Funded PhD Program

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].

Application Deadline: 2025-01-31

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