Alt 2026 Apprenti(e) En Intelligence Artificielle Et Quantification D’incertitudes (h/f)
Airbus
Issy-Les-Moulineaux, France
Scientific machine learning models
Uncertainty quantification techniques
Python programming and git
This apprenticeship will consist in integrating probabilistic methods into Scientific Machine Learning (SciML) models
Job Summary
This apprenticeship will consist in integrating probabilistic methods into Scientific Machine Learning (SciML) models.
The objective is to quantify the confidence associated with predictions and, consequently, to strengthen the robustness of physical modeling in domains such as aerodynamics, mechanics, and electromagnetics.
You will join an international team of 30 people spread across three countries (France, Germany, UK) within the Virtual Product Engineering department.
Matching Summary
This apprenticeship will consist in integrating probabilistic methods into Scientific Machine Learning (SciML) models.