20 computer-science-image-processing Fellowship positions at Nanyang Technological University
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required to substantially advance a research programme focused on next-generation optical imaging and sensing technologies, aimed at pushing the boundaries of performance and enabling new capabilities
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Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a
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necessary lead relevant meetings. To undertake any other duties relevant to the programme of research. Job Requirements: PhD degree in Computer Engineering, Computer Science, Electronics Engineering or
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to the project. Job Requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. Min 3 years of relevant experience in computer vision, artificial intelligence, etc
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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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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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applies AI to tackle challenges in aquaculture and drug delivery, interface of materials science, biology, and computational modeling. Key Responsibilities: Lead and execute AI-driven research projects in
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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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engineering, computer science, or related fields. At least 2 years of relevant experiences in similar role / technical hands-on experience in specific scope Advanced signal processing and programming skills
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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