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Internship engineer/Master 2 Embedded Vision, Artificial Intelligence, AI on Edge H/F


Vacancy details

General information

Organisation

The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.

Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.

The CEA is established in ten centers spread throughout France
  

Reference

2022-23743  

Description de l'unité

LIAE: the Embedded Artificial Intelligence Laboratory (LIAE) is in charge of implementing embedded perception solutions integrating innovative computing architectures (AI/CNN) coupled with multimodal sensor sets (imager-based). The lab is located in the Paris region (Palaiseau).

Position description

Category

Mathematics, information, scientific, software

Contract

Internship

Job title

Internship engineer/Master 2 Embedded Vision, Artificial Intelligence, AI on Edge H/F

Subject

Implementation study of a Recurrent Tracking Model for Embedded Systems

Contract duration (months)

6

Job description

At CEA LIST, the Embedded Artificial Intelligence Laboratory (LIAE) is developing an embedded perception platform designed to dynamically understand the surrounding world and enable real-time navigation. The design of these modern high-tech systems requires a wide range of skills and experience from software, such as innovative computer architecture models (AI/CNN), to hardware including the integration of multimodal sensors.Robust tracking requires knowledge and understanding of the object: its appearance, its movement and its evolution in time. The main objective of this internship is to propose a model to predict the trajectories of detected objects, namely the direction of movement and the speed. The first prediction methods consist in describing rules to determine the motion patterns. However, some approaches are computationally expensive and not always suitable. The goal is to investigate and implement neural prediction techniques, in particular the combination of a CNN (Convolutional Neural Network) and a RNN (Recurrent Neural Network) may be a good starting point. The CNN network would be used to extract features from an input image. As for the RNN network, its objective would be to process historical data to anticipate the next movement of the target.

In this context, the objectives of the internship are:

·         State of the art on existing solutions for the hybrid CNN-RNN networks,

·         Choice of a relevant approach and characterization through learning step on a dataset,

·         Proposal of a lightweight network adapted to the constraints of embedded systems,

·         Implementation on embedded chip type: NVIDIA Jetson Orin.

The candidate will work closely with the other perception team members. At the end of the internship, it will be possible to continue the research work on this topic during a PhD thesis.

Applicant Profile

The ideal candidate is a 5th year engineering student or M2 level, with knowledge of neural networks, AI programming, embedded systems and strong mathematical background.

Position location

Site

Saclay

Job location

France, Ile-de-France, Essonne (91)

Location

  Palaiseau

Candidate criteria

Languages

English (Fluent)

Requester

Position start date

01/02/2023