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

Laboratoire/Entreprise : Laboratoire Informatique d’Avignon avec codirect
Durée : 3 ans
Contact : rosa.figueiredo@univ-avignon.fr
Date limite de publication : 2025-12-31

Contexte :
ANR project EVA – EValuating gender policies in academia through the Analysis of scientific collaboration networks.

Sujet :
https://eva.univ-avignon.fr/wp-content/uploads/sites/34/2025/04/offre.pdf

Profil du candidat :
• Master’s degree (or equivalent) in Computer Science, Applied Mathematics, Operations Research, or a related field.
• Strong ability to write and present research clearly.
• Proficiency in Python, R, Julia or C++, with experience in AI and optimization algorithms.
• Good understanding of graph theory, machine learning, and network analysis.
• Ability to work well in an interdisciplinary team.
• Proficiency in English is required, and knowledge of French is an advantage

Formation et compétences requises :

Adresse d’emploi :
LIA, Avignon

Document attaché : 202504251721_offreThesis_EVA.pdf

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

Laboratoire/Entreprise : LIST3N
Durée : 3 ans
Contact : alexandre.baussard@utt.fr
Date limite de publication : 2025-06-24

Contexte :

Sujet :
voir le descriptif de la thèse “High-dimensional learning for automatic and robust target detection in hyperspectral imagery” dans le fichier attaché

Profil du candidat :

Formation et compétences requises :

Adresse d’emploi :
Université de Technologie de Troyes

Document attaché : 202504241217_PhD_target_detection_hyperspectral_imagery.pdf

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

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 real-world systems — such as industrial IoT networks, online marketplaces, and social platforms — produce data that naturally forms temporal graphs, where each interaction represents who interacted with whom, and when. Temporal graphs offer a powerful way to capture the evolving structure of complex systems and are key to detecting critical phenomena like fraud, cyberattacks, or the spread of misinformation. However, building machine learning tools for temporal graphs remains a major challenge, as common notions of similarity — essential for tasks like classification, prediction, or anomaly detection — are not easily defined in this context.

This PhD project tackles that challenge by developing interpretable methods to compare temporal graphs. The core idea is to use tensor decompositions, which naturally represent temporal graphs as three-dimensional arrays and break them down into simpler/elementary building blocks. Such temporal graph “atoms” can be leveraged to identify what fundamental building blocks are common and different across temporal graph instances, resulting in a set of tools that not only enhance machine learning on temporal graphs but also offer insights into the underlying patterns driving complex systems.

Sujet :
This PhD project aims to lay the foundation for interpretable machine learning on temporal graphs by developing similarity metrics that reveal meaningful patterns across time-evolving systems. To achieve this, we pursue three main goals:

Goal 1: A new tensor decomposition for temporal graphs.
We will develop a decomposition method tailored to the unique challenges of temporal graphs — including their binary and sparse nature, multi-scale patterns, and varying node sets or time intervals. Existing approaches fall short in capturing these aspects jointly. Our method will extend recent advances in geometric and coupled decompositions to extract shared and distinct motifs across graphs.

Goal 2: Similarity metrics, machine learning tasks, and toolbox.
Using our decomposition, we will define interpretable metrics that compare temporal graphs based on their structure, dynamics, and scale. These metrics will power machine learning algorithms for clustering, segmentation, change detection, and prediction. All methods will be implemented in a unified Python toolbox.

Goal 3: Applications to real-world data.
We will validate our methods on two domains. On Wikipedia, we aim to uncover patterns behind successful collaborations by analyzing contributor interactions. In Industry 4.0 settings, we will detect abnormal behaviors in sensor networks to identify faults or cyberattacks.

Profil du candidat :
We look for highly motivated candidates with relevant experience in computer science, data science, and graph machine learning. Experience in Python programming and signal processing will be a plus.

Interested candidates are invited to send an e-mail to
• esteban.bautista@univ-littoral.fr
• laurent.brisson@imt-atlantique.fr
• matthieu.puigt@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 : 202504141906_PhD_Data2Laws.pdf