358 computer-programmer-"U.S"-"University-of-St"-"U.S" Fellowship positions in Singapore
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an intellectually stimulating, globally diverse scientific community. Key Responsibilities • Designing and managing a programme of research to meet agreed objectives. • Working closely with experimental
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an intellectually stimulating, globally diverse scientific community. Key Responsibilities • Designing and managing a programme of research to meet agreed objectives. • Working closely with experimental
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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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) to join our research team, focusing on the morphological characterization of biological samples. This role is part of our broader mechanobiology research program, which investigates how cells and tissues
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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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for researchers and engineers to develop next generation quantum computers based on trapped ions. There are multiple positions available at different levels with a diverse set of expertise. This program is funded
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) Programme studies the causes and treatments of cancer and related diseases. The CSCB research groups have diverse programmes in both basic cancer biology and clinical-translational studies, with a special
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University of Singapore invites applications for Research Fellow / Research Associate to be involved in The Integrated Women’s Health Program (IWHP) longitudinal cohort and the MUSE Randomized Control Trial
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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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