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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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, 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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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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of this role is to support and contribute to an industry innovation research project. The Research Engineer will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep
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Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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quantum sensing technologies (e.g., Rydberg atomic sensors) for wireless communications and sensing. Key Responsibilities: Develop quantum-related theories, models, and algorithms for various communications
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 4 days ago
, with application to biomedical flows (e.g. microcirculation) it is now intended to develop an improved algorithm for advanced particle tracking using machine learning. The algorithm should be tested in
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decision making, while you will be capable to apply machine learning and computational algorithms of social choice. This post is associated with following projects: Embedding EDI in the Distribution