243 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at Nanyang Technological University
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Requirements: Preferably PhD in computer science or related field. Expertise in computer programming Knowledge in machine learning Proven research ability as evidenced through a portfolio of publications and/or
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infrastructure security R&D. Job Requirements: A PhD in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline. Proven research track record demonstrated by publications in top
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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community
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on Phase Field modelling and High-Performance Computing (HPC) for geophysical applications. The Asian School of the Environment (ASE) at Nanyang Technological University is an interdisciplinary school
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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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projects. Disseminate research findings through conferences, invited talks, and outreach activities, strengthening NTU’s leadership in infrastructure security R&D. Job Requirements: A PhD in Computer
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Requirements: A PhD in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline Proven research track record demonstrated by publications in top-tier venues (e.g., IEEE S&P
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, machine learning, and life cycle assessment, we aim to create sustainable wearable systems to enhance human well-being. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking
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Computer Science, Electrical & Electronic Engineering, or equivalent. Background knowledge in signal representation/processing, data-driven and machine learning/analysis, esp in climate related topics. Prior
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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