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

Scientifique-chercheur en IA Appliquée / Applied AI Research Scientist

Location:

Montreal, Canada

Thales is a global leader in aerospace, transportation, defence, security and digital security. In all of these sectors, our architects design innovative solutions that make tomorrow possible today. Montreal is a world‑leading AI hub and home to Thales’ new Centre of Research & Technology in Artificial Intelligence Expertise (cort

AIx), in partnership with IVADO and the Vector Institute. The person hired will join this hybrid role in Montreal, working with teams across the organization to develop AI solutions that are secure, robust, trustworthy and certifiable for critical systems.

Vous serez un chercheur talentueux à la recherche de résoudre des problèmes complexes de recherche et de les appliquer à des projets d’apprentissage automatique pour des systèmes critiques. Vous développerez des solutions de pointe intégrant des capacités d’apprentissage machine sûres, fiables et certifiables. Le même rôle est décrit en anglais dans la section suivante.

You will be a talented researcher seeking to solve complex research problems and apply them to machine‑learning projects for critical systems. You will develop cutting‑edge solutions incorporating safe, robust, trustworthy and certifiable machine‑learning capabilities.

• Conduct research in computer vision, with a focus on image/video processing, deep‑learning models, and algorithm development.
• Develop, implement and optimize state‑of‑the‑art computer‑vision models for tasks such as object detection, segmentation, recognition and tracking.
• Conduct pioneering research in frugal learning methods (e.g., few‑shot and zero‑shot learning) to efficiently learn from limited data while ensuring generalization and accuracy.
• Validate and verify the robustness of machine‑learning models for real‑world applications, including experimentation and data analysis.
• Work with cross‑functional engineering teams to translate research breakthroughs into deployable prototypes and production‑ready systems.
• Support deployment of trustworthy and certifiable AI models in critical systems, addressing safety, reliability and compliance.
• Publish research findings at top‑tier conferences and journals and contribute to patent filings.
• Communicate effectively with both technical and non‑technical stakeholders, learning new tools and techniques as needed.
• Collaborate with academic or research organizations (e.g., NSERC, MITACS) and industrial partners, shaping effective research roadmaps.

• Doctorate or master’s degree in computer science, electrical engineering or a related field with a focus on computer vision, machine learning or artificial intelligence.
• Strong theoretical knowledge of machine‑learning fundamentals, supervised/non‑supervised learning, self‑supervised learning, and few‑shot learning.
• Experience developing and deploying computer‑vision algorithms in academic or industrial settings.
• Proficiency in understanding various image‑sensing modalities (RGB, radar, infrared, LIDAR, hyperspectral, multispectral, RGB‑D, thermal, ultrasonic, sonar, structured light).
• In‑depth knowledge of deep‑learning architectures for computer vision (CNNs, Transformers, foundation models) and related techniques.
• Experience designing experiments, refining models, optimizing, and analyzing data.
• Strong programming skills in Python and C++ and experience with ML frameworks such as PyTorch, Tensor Flow, Scikit‑learn and OpenCV.
• Excellent communication, problem‑solving, teamwork and written/ verbal communication skills.
• Fluency in English;
  French is a plus.

• 2–5 years of research experience in robotics, autonomous vehicles or related fields.
• Track record of research publications in top AI and computer‑vision conferences (e.g., NeurIPS, ICCV, ECCV, CVPR, AAAI, PAMI).
• Proven expertise in developing and implementing detection and segmentation algorithms for radar images.
• Fluency in French (spoken and written).

• Schedule:
  
  regular working schedule.
• Physical environment: mostly behind a desk (office or remote) with…