2,025 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" positions in Singapore
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Employee Category: Research Staff Location_ONB: Outram Campus Posting Start Date: 27/01/2026 Job Description The Duke-NUS Centre for Biomedical Data Science (CBDS) serves as a central hub for Duke-NUS
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SPMS conducts cutting-edge research in quantum science and technology, with strengths in quantum information theory, quantum communication, and quantum resource theories. We aim to advance both
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management for at least 2 years after graduation, preferably in the field of education or psychology, research and analysis Experience in data collection Skills to carry out literature reviews Strong skills
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sample integrity and compliance with regulatory requirements. - Assist in the development and optimization of biobanking workflows. 5. Data Management and Analysis: - Compile and organize experimental data
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programmes. Collect and analyse data on education systems in Singapore and internationally to support NUS’s overall admissions strategy. Collaborate with internal and external stakeholders to ensure a smooth
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the mechanisms contributing to sporadic ALS progression, helping to pave the way for novel therapeutic strategies. They will collaborate closely with data scientists and experimental colleagues to ensure thorough
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the Open Science and Research Services function with the Office of Information, Knowledge and Library Services (OIKLS). The successful candidate will provide research services to support NTU researchers in
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HOP Tool (HOP V1) and preliminary tool completion guide. In Phase 2, HOP V1 will be piloted with K2 educators and will be refined into HOP V2 based on psychometric data and stakeholder feedback. In
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phases are as follows: Phase 1 – Literature reviews and research writing. Phase 2 – Survey and experiment design, and IRB applications. Phase 3 – Data collection and pre-processing. Phase 4 – Data analysis
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Corporate Laboratory is seeking to hire a Research Associate. The selected candidate will focuses on analyzing equipment degradation time-series data and developing forecasting workflows using statistical