Freelancer profile translated to English.

Description

Apprentice engineer in embedded systems and digital engineering (ESTACA graduate – 2026), I specialize in AI applied to autonomous systems, embedded perception, and multi-sensor data fusion.

During my studies and R&D experience, I have worked on several projects combining artificial intelligence, robotics, and embedded systems. Notably, I contributed to the development of a collaborative SLAM system for drones, as well as a V2V communication project between robots integrating vision-based obstacle detection (YOLO) and real-time multi-agent coordination.

For the past three years, I have also been an apprentice at Renault Ampere in ADAS validation for SDV (Software Defined Vehicle) platforms. There, I am developing automated analysis tools in Python for processing large amounts of data from test campaigns.

I also worked in the space sector during a research internship at the LASSENA laboratory (ÉTS Montreal), as part of the NESIVA project with Thales Canada and LeddarTech. My work focused on optimal selection of LEO satellites and GNSS/IMU fusion to improve positioning robustness.

I am particularly interested in ambitious technological projects involving AI, robotics, autonomous systems, or space, and I wish to put my skills to use in innovative projects.

Languages

French
Native or bilingual
English
Fluent
German
Basic

Workplace preferences

Remote only

Primarily works remotely

ÉTS MONTRÉAL,
Research Internship
AVIATION AND AEROSPACE
July 2025 - September 2025 (2 months)
Montreal, Canada
Industrial partners: Thales Canada, LeddarTech

Objective: Improve resilient positioning by selecting the best LEO satellite subsets in real time and integrating GNSS/IMU fusion.

Defined a Doppler/GDOP-like selection metric to rank optimal LEO satellite combinations.

Built a real-time pipeline to compute this indicator on nearby Starlink satellites around a receiver.

Developed a GNSS/IMU fusion algorithm (u-blox GNSS + IMU), then tested, tuned, and improved it based on experimental results.
GNSS Satellites Artificial Intelligence Neural Networks Literature Review
Automotive Company
Apprentice Validation
AUTOMOBILE
January 2023 - Today (3 years and 7 months)
Guyancourt, France
Objective: Validation – Software – Programming (Camera,Traffic Sign
Recognition , Intelligent Speed Adaptation)

Identified and formalised operational requirements (IBM DOORS, Codebeamer).

Wrote test plans linked to system requirements.

Coordinated validation resources to ensure on-time execution of validation campaigns (Jira).

Created vehicle simulation scenarios (IPG CarMaker).

Conducted validation tests and test drives (ROS/ROS2, Linux).

Optimised validation test coverage and execution efficiency.

Developed a Python (Tkinter) GUI to streamline and optimise the team’s validation workflow.
Python Programming ROS Test validation