Internship 6 Months In Electromagnetics - Ai H/F - 38

Le CEA est un acteur majeur de la recherche, au service des citoyens, de l'économie et de l'Etat.

Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies pour la médecine du futur, défense et sécurité sur un socle de recherche fondamentale. Le CEA s'engage depuis plus de 75 ans au service de la souveraineté scientifique, technologique et industrielle de la France et de l'Europe pour un présent et un avenir mieux maîtrisés et plus sûrs.

Implanté au coeur des territoires équipés de très grandes infrastructures de recherche, le CEA dispose d'un large éventail de partenaires académiques et industriels en France, en Europe et à l'international.

Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales :

- La conscience des responsabilités
- La coopération
- La curiositéJoin us for an internship!
CEA Tech
As an intern at CEA, you will have the opportunity to work in a world-renowned research environment. Our teams are made up of passionate and dedicated experts, providing a framework conducive to learning and collaboration. You will have access to state-of-the-art equipment and first-rate research resources to carry out your assignments.
YOUR DUTIES:
During this internship, the student will explore two complementary research directions for object recognition and classification based on electomagnetic backscattering.
The first part of the work will focus on pushing the limits of our in-house electro-magnetic tool for multi-static sensing configurations. The candidate will investigate how well it can handle complex scenarios involving multi-dielectric objects compare its results against full-wave simulation benchmarks. A particular effort will also be dedicated to integrating realistic 3D object models (such as STL or voxel representations) into the simulation workflow.
The second part of the internship will emphasize automation and large-scale evaluation. The student will design and implement scripts to run batch simulations, systematically varying object shape, orientation, and frequency to analyse their impact on the backscattered response. The resulting datasets will then serve as the foundation for advanced AI/ML-based classification methods, opening the door to robust and efficient object recognition capabilities.