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Urgent! Gestion de réseau pilotée par l'IA avec de grands modèles LLMs // AI-Driven Network Management with Large Language Models LLMs Job Opening In Saclay – Now Hiring CEA IP. Paris Laboratoire Systèmes Communiquants

Gestion de réseau pilotée par l'IA avec de grands modèles LLMs // AI Driven Network Management with Large Language Models LLMs



Job description

Topic description

La complexité croissante des réseaux hétérogènes (satellitaire, 5G, IoT, TSN) nécessite de faire évoluer la gestion de réseau.

Le Réseau Basé sur l'Intention (IBN), bien qu'avancé, se heurte encore à la difficulté de traduire des intentions de haut niveau en configurations techniques sans ambiguïté.

Ce travail propose de lever ce verrou en exploitant les Grands Modèles de Langage (LLM) comme interface cognitive pour une automatisation complète et fiable.

Cette thèse vise à concevoir et développer un framework IBN-LLM pour créer le cerveau cognitif d'une boucle de contrôle fermée au-dessus du SDN.

Le travail se concentrera sur trois défis majeurs : 1) développer un traducteur sémantique fiable du langage naturel vers les configurations réseau ; 2) concevoir un Moteur de Vérification déterministe (via simulations ou jumeaux numériques) pour prévenir les « hallucinations » des LLM ; et 3) intégrer une capacité d'analyse en temps réel (RAG) pour l'Analyse de Cause Racine (RCA) et la génération proactive d'intentions d'optimisation.

Nous attendons la conception d’une architecture IBN-LLM intégrée aux contrôleurs SDN, ainsi que des méthodologies pour la vérification formelle des configurations.

La contribution principale sera la création d'un modèle basé sur LLM capable d'effectuer la RCA et de générer des intentions d'optimisation en temps réel.

La validation de l'approche sera assurée par un prototype fonctionnel (PoC), dont l'évaluation expérimentale permettra de mesurer précisément les performances en termes de précision, de latence et de résilience.
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The increasing complexity of heterogeneous networks (satellite, 5G, IoT, TSN) requires an evolution in network management.

Intent-Based Networking (IBN), while advanced, still faces challenges in unambiguously translating high-level intentions into technical configurations.

This work proposes to overcome this limitation by leveraging Large Language Models (LLMs) as a cognitive interface for complete and reliable automation.

This thesis aims to design and develop an IBN-LLM framework to create the cognitive brain of a closed control loop on the top of an SDN architecture.

The work will focus on three major challenges: 1) developing a reliable semantic translator from natural language to network configurations; 2) designing a deterministic Verification Engine (via simulations or digital twins) to prevent LLM hallucinations; and 3) integrating real-time analysis capabilities (RAG) for Root Cause Analysis (RCA) and the proactive generation of optimization intents.

We anticipate the design of an IBN-LLM architecture integrated with SDN controllers, along with methodologies for the formal verification of configurations.

The core contribution will be the creation of an LLM-based model capable of performing RCA and generating optimization intents in real-time.

The validation of the approach will be ensured by a functional prototype (PoC), whose experimental evaluation will allow for the precise measurement of performance in terms of accuracy, latency, and resilience.
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Pôle fr : Direction de la Recherche Technologique
Pôle en : Technological Research
Département : Département Intelligence Ambiante et Systèmes Interactifs (LIST)
Service : Service Interactions et Réseaux
Laboratoire : Laboratoire Systèmes Communiquants
Date de début souhaitée : 01-02-
Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Paris (IP Paris)
Directeur de thèse : SARKISS Mireille
Organisme : Telecom SudParis

Funding category

Public/private mixed funding

Funding further details


Required Skill Profession

Computer Occupations



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