Job offer

European Commission; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

L'IMT Atlantique recherche un doctorant pour une thèse sur la gestion des débris spatiaux. En collaboration avec l'Université de Trente, vous contribuerez à des recherches innovantes intégrant l'IA embarquée pour la détection des débris. Ce travail vise à améliorer les méthodes de suivi orbital et la sécurité des satellites, en réponse aux régulations croissantes sur la durabilité dans l'espace.

Qualifications

• Compréhension des dynamiques orbitales et défis liés à l'environnement spatial.
• Expérience en traitement de signaux de capteurs optiques et radars.
• Compétences en développement de modèles d'IA intégrés.

Responsabilités

• Assurer une analyse orbitale en temps réel des débris spatiaux.
• Détecter et suivre les débris embarqués à bord d'un satellite.
• Contribuer à la conception d'algorithmes efficaces pour le traitement embarqué.

Connaissances

Connaissances en ingénierie spatiale

Traitement de signaux

Intelligence artificielle et apprentissage automatique

Vision par ordinateur

Développement d'algorithmes

Simulation et modélisation

Programmation

Travail collaboratif et interdisciplinaire

Innovation et résolution de problèmes

Connaissance des réglementations spatiales

Formation

Master ou équivalent

Description du poste

Organisation/Company IMT Atlantique Department Doctoral division Research Field Engineering » Aerospace engineering Engineering » Computer engineering Engineering » Electrical engineering Engineering » Other Computer science » Other Technology » Remote sensing Technology » Computer technology Technology » Instrumentation technology Technology » Space technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 1 Sep 2025 - 23:00 (Europe/Paris) Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Nov 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Thesis supervisors : Matthieu ARZEL (IMT Atlantique), Lorenzo BRUZZONE (University of Trento) and Thomas BOUTERAON (Irispace)

Joint thesis with the University of Trento - Italy

IMT Atlantique, internationally recognised for the quality of its research, is a leading general engineering school under the aegis of the Ministry of Industry and Digital Technology, ranked in the three main international rankings (THE, SHANGHAI, QS).
Located on three campuses, Brest, Nantes and Rennes, IMT Atlantique aims to combine digital technology and energy to transform society and industry through training, research and innovation. It aims to be the leading French higher education and research institution in this field on an international scale. With 290 researchers and permanent lecturers, 1000 publications and 18 M€ of contracts, it supervises 2300 students each year and its training courses are based on cutting-edge research carried out within 6 joint research units: GEPEA, IRISA, LATIM, LABSTICC, LS2N and SUBATECH. The proposed thesis is part of the research activities of the 2AI and Cosyde teams at Lab-STICC and the Mathematical and Electrical Engineering (MEE) department of IMT Atlantique. The scientific activities of this department cover a broad spectrum including telecommunications, signal and information processing, electronics, and artificial intelligence.
This thesis will be conducted jointly with the University of Trento (Italy) as part of the Italian doctoral program SST, specializing in space sciences and technologies. The UniTrento Remote Sensing Laboratory (https://rslab.disi.unitn.it/ ) is a famous Italian
laboratory on both Earth Observation and Planetary Exploration. RSLab has the leadership of important projects (e.g., the radar for icy moon exploration (RIME) currently in the cruise phase toward the Jupiter system on the ESA JUICE mission, the subsurface radar sounder (SRS) on the ESA EnVision mission to be launched to Venus in 2031, the ESA Climate Change Initiative High Resolution Land Cover project) and is involved in many other research initiatives.
The supervisors will be:
- Matthieu Arzel, Professor in the MEE department at IMT Atlantique ( https://www.imt-atlantique.fr/fr/personne/matthieu-arzel ),
- Thomas Boutéraon, Doctor in Astrophysics, Head of the University Space Center (CSU) at Irispace,
- Lorenzo Bruzzone, Professor at the University of Trento ( https://webapps.unitn.it/du/en/Persona/PER0004714/Curriculum ), Head of the Remote Sensing Laboratory ( https://rslab.disi.unitn.it/ ).

Scientific context

The SENSIAE project addresses the growing issue of space debris, a strategic challenge for New Space and sustainable orbit management. While the ESA has made Zero Debris a key pillar of its strategy, tracking objects in orbit remains a complex challenge, requiring efficient detection, classification, and tracking methods. It is estimated that there are about one million debris pieces ranging from 1 to 10 cm in size. Their velocity, which can exceed 10 km/s, poses a significant threat to operational satellites.
By integrating debris detection and tracking directly onboard satellites via embedded AI, the project addresses these challenges and anticipates future international regulations on sustainable space management. Numerous scientific and technical hurdles remain, but these solutions will be crucial when global legislation mandates strict orbital cleanup rules—a field in which Europe plays a leading role.
Current solutions based on ground-based telescopes suffer from limitations such as atmospheric disturbances, partial orbital coverage, and difficulty detecting smaller debris. Led by IRISPACE, SENSIAE proposes an innovative alternative by integrating sensors and frugal AI directly onboard satellites. This system must be optimized as a whole, from sensor design to the development of robust and energy-efficient algorithms which are core research areas of Lab-STICC.

Expected contributions of the Thesis

The goal of this PhD thesis is to ensure reliable real-time orbital analysis despite the diversity of debris (size, speed, distance, material), enabling debris mapping and, ultimately, allowing satellites to autonomously perform collision-avoidance maneuvers.
To do that, the PhD student will study the design of an embedded AI processing signals provided by different sensors (optical and radar for instance) to detect and track debris on board a satellite. This study will benefit from preliminary works done in IMT Atlantique on radar design for such a use case and simulation environment (based on Unreal Engine) to train AI models and evaluate solutions based on computer vision.
Additionally, IRISPACE-affiliated companies such as Unseenlabs and Syrlinks could leverage these innovations in future products. These players in space surveillance and secure communications could benefit from the project’s advancements to strengthen their positioning in the advanced space technology market.

Application
To apply for this position, please send a detailed application including a cover letter, an up-to date CV, transcripts of grades and reference letters. These documents will be adressed to [e-mail]

Where to apply

E-mail [e-mail]

Requirements

Research Field Engineering » Computer engineering Education Level Master Degree or equivalent

Skills/Qualifications

For a project like SENSIAE, which involves detecting and tracking space debris using advanced technologies such as embedded artificial intelligence and various sensors, a PhD student should ideally possess or develop the following skills. It is not necessary to master all these skills at the beginning of the thesis; additional training will be provided as part of the doctoral program.:
1. **Space Engineering Knowledge**: Understanding orbital dynamics, challenges related to the space environment, and satellite technologies.
2. **Signal Processing**: Experience in processing signals from optical and radar sensors, which is crucial for detecting and tracking objects.
3. **Artificial Intelligence and Machine Learning**: Skills in developing and training AI models, particularly for embedded and real-time applications.
4. **Computer Vision**: Knowledge of computer vision techniques and frameworks (as Pytorch) for image analysis and object detection.
5. **Algorithm Development**: Ability to design robust and energy-efficient algorithms for embedded processing.
6. **Simulation and Modeling**: Experience with simulation environments, such as Unreal Engine, to test and validate models and algorithms.
7. **Programming**: Proficiency in relevant programming languages such as Python, C++, and possibly languages specific to embedded applications.
8. **Collaborative and Interdisciplinary Work**: Ability to work in a collaborative research environment, often with interdisciplinary teams including engineers, scientists, and industry partners.
9. **Innovation and Problem-Solving**: Ability to innovate and solve complex problems, often with limited resources.
10. **Knowledge of Space Regulations**: Understanding of regulations and issues related to sustainable space management.
These skills will enable the PhD student to contribute effectively to the project and address the scientific and technical challenges associated with space debris management.

Languages ENGLISH Level Good

Internal Application form(s) needed

Space environment monitoring using embedded artificial intelligence.pdf