About the position
About the research centre or Inria department The Inria center at the University of Rennes is one of eight Inria centers 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 ecosystem of R&D and innovation, including highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, centers of excellence, and technological research institutes. Context The PhD will be hosted at the IRISA/Inria Centre at the University of Rennes , a major and recognized player in the field of digital sciences. The centre comprises more than thirty research teams and is at the heart of a rich R&D and innovation ecosystem. The position will be based on the Beaulieu Scientific Campus of the University of Rennes, a medium-sized town with an intense student life (approximately 25% of the population). Rennes is a dynamic, lively city and a major centre for higher education and research in France. The PhD will be hosted by the ERMINE project-team (Measuring and Managing Network operation and economics), a joint team between Inria and IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires), in collaboration with the ADOPNET team . Working conditions : - Partial reimbursement of public transport costs - 7 weeks of annual leave plus 10 extra days off (RTT – statutory reduction in working hours) - Possibility of teleworking (90 days per year) and flexible organization of working hours - Access to training, cultural and sports activities, and vocational training The GENIE ANR project (Network Optimization and Generative Intelligence Ecosystem) aims to revolutionize network infrastructure management by combining the strengths of Large Language Models (LLMs) with network domain-specific expertise. The project, funded by the French National Research Agency (ANR), involves partners including University of Rennes, IMT, LEAT, L3i, and LabHC. GENIE addresses the limitations of conventional techniques by enabling an interpretable and adaptable approach to network optimization and automated management. The project will design an LLM pipeline for network management, studying collaborative and scalable LLM-based strategies that enable parallel processing, including a consensus mechanism to maintain effective decision-making. Assignment Context and Problem Statement As networks evolve to support ultra-reliable, low-latency communications (URLLC) and complex virtualization paradigms (e.g., 5G/6G Network Slicing), they are becoming increasingly dynamic and opaque. This creates a severe "visibility gap" where operators struggle to diagnose faults without direct administrative access to the underlying infrastructure. Network tomography provides a vital tool to achieve observability by inferring hidden link metrics from end-to-end measurements. However, traditional algebraic and statistical methods suffer from rigidity and scalability issues. Recent advancements have demonstrated that Machine Learning, specifically Relational Graph Convolutional Networks (RGCNs) operating on line graphs, can learn shared link relations and generalize monitor selection. Despite these successes, significant open challenges remain. First, in contrast to most existing literature that relies heavily on synthetic or idealized simulations, there is a critical need to evaluate and generalize these models across entirely different, unseen topologies using real or highly realistic network data. Furthermore, handling non-additive metrics (such as congestion or binary link failures) under these realistic conditions requires more sophisticated architectures. Second, while Graph Neural Networks (GNNs) can accurately infer *where* a degradation occurs mathematically, they lack the administrative and operational context to explain *why* it is happening. This thesis proposes a novel framework that bridges the mathematical inference capabilities of GNNs with the contextual reasoning of Large Language Models (LLMs) to achieve true cognitive network observability. It is conducted within the framework of the GENIE ANR project. Assignments: The PhD student will be responsible for conducting full-time research activities centred on the theme of the thesis: cognitive network observability through the integration of Graph Neural Networks and Large Language Models. The specific assignments include: 1. Advanced GNN Development: Design and implement Relational Graph Convolutional Networks (RGCNs) and novel message-passing paradigms capable of inferring non-additive network metrics and achieving zero-shot transfer to unseen topologies. 2. Graph-RAG Framework Design: Develop a Retrieval-Augmented Generation (RAG) framework tailored for network graphs, including the construction of a vector database containing historical incident tickets, BGP routing logs, maintenance schedules, and vendor documentation. 3. LLM Integration and Evaluation: Integrate the GNN inference pipeline with an LLM equipped with the Graph-RAG architecture, and evaluate the system's ability to generate human-readable, context-aware root cause diagnostics. 4. Experimental Validation: Conduct extensive experiments on real or highly realistic network data to validate the generalizability and robustness of the proposed framework across diverse, unseen topologies. 5. Scientific Dissemination: Write and publish research results in international peer-reviewed conferences and journals, and present findings at scientific events. The PhD student will be supervised by a researcher within the ERMINE team and will benefit from collaboration opportunities within the GENIE ANR project consortium and with the ADOPNET team. Main activities The main activities of the PhD student will include: - **Conducting research** on GNN-based network tomography and LLM-driven explainable diagnostics, including analysis and synthesis of the state of the art. - **Developing experiments** to evaluate model performance across cross-topology generalization and non-additive metric inference scenarios. - **Developing software solutions**, including prototype implementations of the Graph-RAG pipeline and GNN architectures. - **Analyzing research situations** and interpreting experimental results to guide the research direction. - **Writing scientific documents**, including conference papers, journal articles, and the PhD thesis manuscript. - **Presenting research work** at internal seminars, project meetings, and international conferences. - **Collaborating** with project partners within the GENIE ANR consortium and participating in project meetings and collaborative research activities. - **Contributing to the scientific life** of the ERMINE and ADOPNET teams and the Inria Rennes research centre, including participation in seminars and working groups. ## Proposed Research Axes ### Axis 1: Advanced GNNs for Cross-Topology Generalization and Complex Metrics This first axis focuses on pushing the boundaries of GNN-based network tomography to ensure robust performance across diverse, real-world network deployments. - **Zero-Shot Transfer to Unseen Topologies:** Moving beyond generalizing within a single network, this research will investigate zero-shot transfer learning techniques. The objective is to apply models trained on smaller, controlled topologies directly to large-scale, heterogeneous architectures without retraining. - **Non-Additive Metric Inference:** Expanding the GNN's capabilities to infer non-additive metrics, such as bandwidth bottlenecks or binary link states. This will require the development of novel message-passing paradigms capable of capturing complex, non-linear end-to-end relationships. ### Axis 2: Graph-RAG for Explainable Network Diagnostics To transform the raw mathematical inferences of the GNN into actionable, explainable insights, this axis integrates a Retrieval-Augmented Generation (RAG) framework tailored for network graphs. - **Knowledge-Augmented Tomography:** Development of a vector database containing unstructured and semi-structured operational data, including historical incident tickets, BGP routing logs, maintenance schedules, and vendor documentation. - **Explainable Root Cause Analysis:** When the GNN pipeline infers an anomalous metric (e.g., a delay spike) on a specific hidden link, this graph-structured anomaly is fed into the LLM. Using the RAG architecture, the LLM will query the operational database to correlate the mathematical anomaly with real-world events. - **Expected Output:** Instead of a simple inference matrix, the system will generate human-readable diagnostics (e.g., *"The GNN inferred a 40ms delay spike on Link X. Based on retrieved maintenance logs, this correlates with a recent firmware update on the adjacent virtualized router."*). ## Expected Impacts This research will position network observability at the intersection of Network AI and Generative AI. By utilizing GNNs as the "eyes" of the system for inference, and RAG-equipped LLMs as the "brain" for contextual reasoning, this thesis will deliver a highly generalizable, self-explanatory framework. This represents a critical leap toward zero-touch network automation and resilient 6G infrastructure management. Skills Machine Learning & Graph Neural Networks: Deep understanding of GNNs, message passing, relational graph convolutional networks, and transfer learning. Network Tomography & Networking: Knowledge of network measurement, performance metrics, 5G/6G, network slicing, and virtualization. Large Language Models & RAG: Experience with LLMs, prompt engineering, vector databases, and Retrieval-Augmented Generation. Programming & Tools: Proficiency in Python, PyTorch/TensorFlow, network simulation tools, and data analysis. Research & Communication: Scientific writing, presentation skills, and ability to collaborate in a multidisciplinary environment. Soft Skills: Autonomy, curiosity, perseverance, and teamwork.
This listing was collected from a public source and is reproduced here for
information only. Always confirm the details on the original posting before applying.
View the original posting