Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LISIC – Univ. Littoral Côte d’Opale
Durée : 3 years
Contact : esteban.bautista@univ-littoral.fr
Date limite de publication : 2025-06-01

Contexte :
Many complex systems — such as the Internet, transportation networks, and financial systems — produce data that naturally takes the form of temporal graphs, where each link between nodes is time-stamped. Temporal graphs allow us to model and analyze interactions over time, such as network traffic between computers, scheduled trips between stations, or transactions between bank accounts. A common challenge in working with such graphs is identifying subsets of the temporal graph that optimize certain properties, like density, cost, duration, etc. These tasks are essential for applications such as anomaly detection, cybersecurity, or route planning, but they typically involve NP-hard combinatorial problems, making them impractical to solve exactly.

This PhD project explores a new direction for tackling these problems using artificial intelligence. While heuristic methods exist, they often struggle to balance speed and accuracy in temporal graph settings. In contrast, recent advances show that AI models can be trained to solve combinatorial problems on static graphs efficiently, yet their potential remains largely unexplored in the temporal graph setting. This project aims to bridge that gap by developing AI-based methods that learn to solve combinatorial optimisation problems emerging on temporal graphs.

Sujet :
This PhD project aims to explore the potential of machine learning methods as a means to solve combinatorial optimization problems on temporal graphs. We target three specific goals:

Goal 1: End-to-end learning framework.
We aim to design a framework that trains neural models to directly map problem instances to solutions in temporal graphs. While such approaches exist for static graphs, our challenge is to extend them to the temporal setting by defining suitable loss functions and training strategies.

Goal 2: A novel filter-based architecture.
We plan to develop a neural architecture that treats optimization as a filtering task — discarding irrelevant links to isolate the optimal subgraph. Building on spectral methods and recent work in temporal graph signal processing, we will explore how filters can be effectively defined and learned in a frequency-structural domain.

Goal 3: High-impact applications.
We will validate our methods on two key applications:

– Anomaly localization: Many systems detect anomalies but fail to pinpoint their origin. We aim to learn to localize anomalies without relying on assumptions about their structure.

– Temporal graph exploration: In transportation networks, finding optimal exploration routes is NP-hard. Our goal is to develop practical AI-based methods that scale better than current approximations.

Profil du candidat :
We look for highly motivated candidates with relevant experience in computer science, graph algorithms, and/or
deep learning. Experience in Python programming and operations research will be a plus.

—— Application ——-

Interested candidates are invited to send an e-mail to

• esteban.bautista@univ-littoral.fr
• rym.guibadj@univ-littoral.fr
• gilles.roussel@univ-littoral.fr

while attaching the documents that can support their application:

• your resume;
• a cover letter;
• your transcripts from the last year of B.Sc to the last year of M.Sc. (if the latter is already available);
• two reference letters or the names and means of contact of two academic advisers.

Applications will be reviewed on a rolling basis until the position is filled.

Formation et compétences requises :

Adresse d’emploi :
LISIC Laboratory – Université du Littoral Côte d’Opale – Site Saint-Omer (Hautes de France), France

Document attaché : 202504141925_PhD_COTEG.pdf