Graph Neural Networks for Smart Radar sensors

Job description: Perception and analysis of the environment around us is a major challenge in many promising industrial fields. In this context, artificial intelligence (AI) algorithms have unquestionably proven their effectiveness for vision-related tasks, using various sensors (camera, lidar...). Today, there is growing interest in the use of radar (radio detection and ranging) sensor data by AI. Radar is a sensor that stands out for the nature of its data, its operability (low light levels, bad weather, etc.) and its cost. However, it produces sparse data with low spatial resolution, making them difficult to exploit by traditional algorithms. Recently, artificial neural networks based on a graph representation of data (Graph Neural Networks - GNN) have shown good accuracy on sparse and noisy sensor data [1]. As a result, the use of GNNs to exploit radar data seems very promising [2]. The spectrum of applications is wide, from intelligent vehicles (cabin monitoring) to medical devices (vital signs measurement) and surveillance devices (fall detection). [1] Dalgaty et al, « HUGNet: Hemi-Spherical Update Graph Neural Network applied to low-latency event-based optical flow » Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 3952-3961 [2] Fent, et al., "RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 182-191.

Institute description: The CEA is a leading research institute and a major player in the fields of energy, information, health and defense. As a specialist in intelligent digital systems, CEA-LIST's main mission is research and innovation, with the aim of transferring technology to industry. The internship will take place at CEA-LIST, in the Integrated Multi-Sensor Intelligence Laboratory (located in Grenoble), which brings together experts in artificial intelligence, embedded systems and sensors.

Your profile: Profil recherché : Etudiant(e) en dernière année d’Ecole d’Ingénieur ou Master 2 Compétences souhaitées : Une forte motivation pour apprendre et contribuer à la recherche en intelligence artificielle. Une connaissance approfondie en informatique et langages de programmation (Python). Des connaissances en intelligence artificielle et une expérience dans les réseaux de neurones artificiels (librairies Pytorch ou Tensorflow) sont un plus. L’entretien de recrutement pourra faire référence aux deux publications citées. Conformément aux engagements pris par le CEA en faveur de l’intégration de personnes en situation de handicap, cet emploi est ouvert à tous et toutes.

Level of qualification studied: Bac+5 - Master 2