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feeds Stage Human-Object Interaction benchmarking-Saclay-H/F 174 / 723 vacancy Détail de l'offre Description du poste Domaine Mathématiques, information scientifique, logiciel Contrat Stage Intitulé
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feeds M2 Internship – Perturbation-based Bayesian learning with resistive memories 178 / 815 vacancy Vacancy details Position description Category Micro and nano technologies Contract Internship Job
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feeds Dynamic Distribution Shifts: OoD Detection with Dynamic Thresholds H/F 36 / 669 vacancy Détail de l'offre Description du poste Domaine Systèmes d'information Contrat Stage Intitulé de l'offre
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between the elements composing the object. Since neutron attenuation primarily results from interactions with atomic nuclei, the underlying physics is fundamentally different from that of X-rays
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and Pattern Recognition (CVPR), 2016. [8] TorchVision / PyTorch documentation, ResNet-18 and ResNet-50 Model Architectures. [9] Ultralytics, YOLOv8 Real-Time Object Detection and Image Segmentation
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the gap between state-of-the-art generative grasp planners and real-world physical execution. The core objective is to develop a robust robotic skill layer — integrating 6D object pose estimation with
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cadre d'étude s'inscrit dans celui de la planification optimale d'expériences bayésienne (Bayesian Optimal Experimental Design) [2,3]. Les missions du stage s’articulent autour de deux thématiques
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, lidar, etc.). Today, there is a growing interest in the use of AI for radar sensor data (radio detection and ranging). Radar is indeed a sensor that stands out due to the nature of its data, its
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comparer leurs performances. D’autres algorithmes seront susceptibles d’être explorés. L’ensemble des développements s’effectuera en Python. Références : [1] J.R. Allison et al., A Bayesian approach
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problems plays a key role in many fields, ranging from object characterization and inspection to radar stealth. A common approach consists in transforming the initial 3D volumetric problem into a 3D