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training and have opportunities to participate in faculty development programmes. The fellowship is tenable for one year. On an exception basis, a two-year programme may be supported. Service Commitment One
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) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena
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research documentation, including technical reports, conference/journal papers, and research grant progress updates. Job Requirements: Ph.D. degree in Computer Science, Electrical/Electronic Engineering
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RESEARCH FELLOW IN ARI(TRANSITION RISKS IN IMPLEMENTING FOREST CARBON INITIATIVES IN SOUTHEAST ASIA)
financing models can be developed and tested to support the equitable distribution of benefits while minimizing economic disruptions to traditional livelihoods? How can new digital technologies and AI be
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; Assessment of carbon pricing and carbon markets. The above list is not exhaustive, moreover, at any given point in time the institute undertakes multiple projects in parallel and opportunity will exist
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structural integrity and stress distribution of modular floating units under operational and extreme conditions. • Optimize design configurations to enhance performance and durability. 3. Stability and Mooring
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Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in
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Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) 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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, 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