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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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, 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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Responsibilities: The successful applicant will be responsible for: Obtaining theoretical results at the interface of geometry and biophysics Designing, implementing, and testing algorithms to model active matter
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learning algorithms (Deep learning, Reinforcement learning, etc.); Proficiency in written and spoken English - essential for data analysis and communication with stakeholders Excellent oral communication
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, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets. Collaborate with multidisciplinary teams including biologists, engineers, and clinicians
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(terrestrial and NTN). The goal of this research is to design and develop algorithms and techniques that adapt to the environment, minimizing signaling overhead associated with channel estimation and enhancing
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-making algorithm for autonomous vehicles. The work will involve sensor fusion, perception, trajectory prediction and test rig set-up, and experimental validation. Job Requirements: PhD Degree in Vehicle
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” wherein messages including emotions are incorporated into the movements of collaborative robots. Key Responsibilities: Design, implement, and test real-time multi-objective motion planning algorithms
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on high-speed vision perception for autonomous driving. This project aims to advance the state of the art in visual perception algorithms and real-time systems for autonomous racing, pushing the boundaries