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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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on AI-driven end-to-end autonomous driving algorithms. Key Responsibilities: The research fellow will be leading the development of AI-driven end-to-end autonomous driving algorithms. The work will
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recommendation mechanisms based on semantic analysis and natural language processing, with the aim of facilitating collaboration and convergence of proposals. Developing and training NLP algorithms in multiple
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based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa. UM6P is an institution oriented towards applied research and
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Fluid-Structure Interaction to focus on developing computational algorithms for fluid-structure interaction problems including the multiphysics effect of electromagnetism. The Role: As a Research Fellow
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challenges: Successful candidates will contribute to one or more of the following research domains: development of autonomous navigation and path planning algorithms for lunar terrain traversal and regolith
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) Design robust obstacle avoidance algorithms for mobile robots in dynamically changing environments, focusing on formal safety constraints and real-time performance in unpredictable conditions. b) Develop
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for the position of Research Fellow for the project Anticipatory Robotics. Project Introduction: The project titled Anticipatory Robotics aims to improve Human-Robot Interaction by developing a “Robot Body Language
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 15 days ago
multidisciplinary team involving image processing labs (e.g., CMAP) and French hospitals (e.g., CHU Kremlin-Bicêtre, Hôpital Foch, Institut Mutualiste Montsouris, Institut Gustave Roussy). Phase 1: Design and develop
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions