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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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friendly and international work environment Learn more about CQT at https://www.cqt.sg/ Job Description We are seeking a strong quantum computing researcher to develop quantum algorithms for generative
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/ machine learning algorithms to support research in the IDMxS Analytics Cluster. The RF will apply/ improve machine learning algorithms to process (e.g., classify, predict) data collected by IDMxS. Help
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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
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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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operation. Develop modular architectures for multi-agent coordination, sensing, and communication. Integrate sensor suites, flight controllers, and swarm coordination algorithms into UAV platforms. Conduct
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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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a Research Fellow to contribute to a project focused on algorithm design in Game Theory and Fair Division. Key Responsibilities: Formulate mathematical models for research problems in computational
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noise, allowing only specific algorithms with relatively shallow quantum circuits to be executed. In the NISQ era, hybrid algorithms run partially on quantum computers and partially on classical computers
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techniques (especially program synthesis) to improve the explainability and robustness of AI models Participating in co-operation with Continental’s development team Interacting with Continental business areas