Machine Learning Engineer - Internship for 6 months
Machine Learning Engineer - Internship for 6 monthsWHO ARE WE?
Join Thales , global leader in safety and security technologies for Aerospace, Ground Transportation, Defence and Security sectors. With 62,000 employees, in 56 countries, the Group enjoys an international implantation which allows it to act as close as possible to its customers, everywhere in the world.
Thales Alenia Space , a joint venture between Thales (67%) and Leonardo (33%), is a key European player in space telecommunications, navigation, Earth observation, exploration and orbital infrastructures.
Because of its unrivaled expertise in dual (civil/military) missions, constellations, flexible payloads, altimetry, meteorology and high-resolution optical and radar instruments, Thales Alenia Space is the natural partner to countries that want to expand their space program.
The company posted consolidated revenues of 2.1 billion euros in 2015, and has 7,500 employees in eight countries.
WHO ARE YOU?
- Engineer student specialized in Machine Learning
-Autonomous, dynamic and proactive, you want to evolve within teams passionate about innovation
Knowledge : Data mining and Machine Learning
You have strong skills in python and C ++.
WHAT COULD WE ACCOMPLISH TOGETHER?
The new emergence of Machine and Deep Learning technologies opens new possibilities for the industry, especially because it comes along with the democratization of Clouds solutions, GPU tools, and programming frameworks.
Within the R & D Innovation team, you will be studying the use of machine learning to optimize existing industrial systems. You will particularly be involved in designing prototypes to integrate deep learning solutions (Tensor Flow, Torch, ...) into innovative projects. As a background task, you will study the optimization of algorithms performance using hardware in the best way possible (GPU, multi-core).
Within the EPSIS project you will optimize the existing algorithms and develop new software bricks to detect surface defects present on satellite elements during their production.
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