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, mapping surface changes due to disaster events, and mapping ocean colours and ocean topography for carbon flux estimates. We are also interested in candidates who have experience applying machine learning
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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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. Job Requirements: PhD degree in Computer Science, Computer & Electronics Engineering or other related fields. Strong background and knowledge in at least one or preferably more of the following fields
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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programming languages such as C and Python Proficiency in deep learning frameworks such as Pytorch and Tensorflow Knowledge in imaging and computing device and equipment Good written and oral
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research in artificial intelligence, machine learning system, edge computing. To produce research papers and reports as required by the funding body or for dissemination to the wider academic community
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research in artificial intelligence, machine learning system, edge computing. To produce research papers and reports as required by the funding body or for dissemination to the wider academic community
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resilient (urban) model for other cities to learn – in an increasingly precarious environment of climate change and (un)sustainable practices. Secondly, this project will explore how Singapore also learns
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on emerging privacy-preserving techniques such as homomorphic encryption, secure multi-party computation and federate learning. Key Responsibilities: Conduct advanced research in the areas of privacy-preserving