830 computer-programmer-"IMPRS-ML"-"IMPRS-ML"-"IMPRS-ML" positions at Nanyang Technological University
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Plan, coordinate, and execute credit-bearing career courses with tracking of students on their completion. Implement course planning, scheduling, operational support, programme coordination and logistics
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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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, tsunamis, and climate change in and around Southeast Asia, towards safer and more sustainable societies. The Climate Transformation Programme (CTP) aims to develop, inspire and accelerate knowledge-based
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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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The School of Social Sciences, Sociology Programme is looking for Part-time Lecturers (PTL) who will be responsible for conducting tutorials and assessments for a course. We invite applications
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, accurate programme information. Assist with the planning and coordination of overseas outreach events Coordinate and work with students for marketing, admissions and recruitment related-activities
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Young and research intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. The NTU-University Scholars Programme (NTU-USP) leads a
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of academic programmes within the assigned department Key Responsibilities Managing program budgets, resource allocation, and financial reporting to support departmental objectives. Developing and implementing
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Nanyang Technological University’s National Centre for Research in Digital Trust (DTC) is a Trust Technology Research Centre to execute a national program to help put Singapore into a strong trust
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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