Research Fellow in "Drones and Artificial Intelligence: security

Lieu de travail Villeneuve d'Ascq - Les Hauts de France - France Champ scientifique principal Mathématiques Champs scientifiques secondaires

• Sciences de l'ingénieur
• Télécommunications

Mots clés Multimodal data analysis, innovative AI architectures, edge computing, federated learning Fonction Recherche et Développement

Employeur

Université Gustave Eiffel is a major French public research university dedicated to sustainable cities, mobility, infrastructure and complex systems. It combines academic excellence with applied research addressing key societal and technological challenges.

The position is hosted within the COSYS Department (Components and Systems), at the LEOST laboratory (Lille campus). The research environment is interdisciplinary, bringing together expertise in applied mathematics, artificial intelligence, signal processing, wireless communications and cybersecurity. The laboratory maintains strong national and European collaborations and offers a dynamic, research-intensive environment.

Site web :

https://leost.univ-gustave-eiffel.fr

Poste et missions

The successful candidate will develop innovative research in Applied Mathematics and Artificial Intelligence for complex, dynamic and data-intensive systems.

Research activities will include:

• Mathematical modelling and statistical learning for heterogeneous and multimodal data,
• Design of advanced machine learning architectures adapted to constrained or distributed environments (edge computing, federated learning),
• Development of robust and reliable AI methods for dynamic and non-stationary contexts,
• Contribution to cybersecurity challenges in communicating systems, including drone detection and protection.

The researcher will actively contribute to the scientific life of the laboratory through high-level publications, supervision of PhD students, participation in national and European research projects, and development of academic and industrial partnerships.

• PhD in Applied Mathematics, Statistics, Machine Learning, Computer Science, or a closely related field,
• Strong expertise in mathematical modelling, optimization, statistical learning, or deep learning,
• Ability to connect theoretical developments with real-world system constraints,
• Proven track record of high-quality scientific publications,
• Demonstrated capacity for interdisciplinary collaboration.

Experience or interest in signal processing, wireless communication systems, or cybersecurity of communicating systems will be considered an asset but is not mandatory.