Applied AI Research Scientist​/Scientifique-chercheur en IA Appliquée

A Research and Technology (R&T) scientist specializing in frontier AI and computer vision is responsible for discovering, developing and integrating innovative methodologies to address complex challenges in these fields and to translate related concepts into Thales solutions. He/she proves their viability through the implementation of Proofs of Concept (PoCs) and guides their maturity growth through prototypes and demonstrators that will in turn illustrate and enable their full business potential.

To be successful in this role, curiosity for what is new, willingness to challenge the status quo, open-mindedness and out-of-the-box thinking are crucial. The individual should quickly learn and assess new techniques and technologies in order to decide whether to adopt, adapt or discard them. He/she also needs to be able to come up with new ideas, present them, challenge them and improve them continuously.

The individual must possess deep, hands-on technical skills and be familiar with computer vision and AI for computer vision research methods and tools
. The candidate must demonstrate a strong understanding of the scientific method and his/her ability to deliver quality content at all steps of the research process, from the capability to summarize the theoretical background of a novel domain to the dissemination of research outputs through peer-review publications, patents or other means.

The scientist is part of a multi-disciplinary team. Strong team spirit and teamwork capabilities are mandatory. The ideal candidate therefore is able to demonstrate transverse skills that would facilitate the transfer of the work carried out to all stakeholders involved. She/he will contribute as a technical subject matter expert to Research and Technology projects across Thales and its business units. Therefore, good communication skills are required.

• Conduct research in computer vision, focusing on vision-language foundation models (VLMs, Ve-LLMs, and vision foundation models), image/video processing, efficient and training-free learning algorithms, and edge-optimized CV models.
• Design, adapt, and evaluate foundation models, vision-language models, and multimodal perception models for real-world use cases.
• Develop methods for object detection, segmentation, visual reasoning, tracking, and scene understanding.
• Conduct pioneering research in frugal learning methods to adapt large-scale models using fine-tuning, few-shot learning, zero-shot learning, prompt tuning, LoRA, or other parameter-efficient techniques.
• Establish rigorous evaluation protocols for accuracy, robustness, uncertainty, bias, explainability, and generalization.
• Translate state-of-the-art research into prototypes, demonstrators, and deployable AI components.
• Collaborate with cross-functional engineering teams to translate research breakthroughs into deployable prototypes and production-ready systems.
• Work on problems of diverse scope to support the deployment of trustworthy and certifiable AI models in critical systems.
• Publish research findings in relevant/top-tier conferences and journals, in AI and computer vision, and contribute to publishing innovation patents.
• Demonstrate excellent communication skills with both technical and non-technical stakeholders.
• Demonstrate good judgment in selecting methods and techniques for obtaining better solutions.
• Develop excellent collaboration skills with academic or research organizations (e.g., NSERC, MITACS, etc.), and with industrial partners.
• Suggest, collect, and synthesize system requirements from stakeholders and contribute to shaping effective research and innovation roadmaps.

• Ph.D. or master’s degree in computer science,…