179 postdoc-parallel-computing Fellowship research jobs at Nanyang Technological University
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Research Fellow / Associate Research Fellow / Senior Analyst / Research Analyst (Maritime Security Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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Associate Research Fellow / Research Fellow (Military Transformations Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang Technological University
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Research Analyst/Senior Analyst/Associate Research Fellow (China Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang Technological University, Singapore
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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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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maritime transport, marine technology, computer science, or a related field; Excellent programming skills, such as Python, Matlab, C++, or other computer languages; A record of publications in reputable peer
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applies AI to tackle challenges in aquaculture and drug delivery, working at the interface of materials science, biology, and computational modeling. Key Responsibilities: Lead and execute AI-driven
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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