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and external. Provide programmes needs analysis, proactively tracking programme success and iterate on programme design as necessary using a data driven approach. Serve as an active ambassador for our
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computational materials science techniques (DFT, MD, machine learning force field modelling) with data-driven approaches. Work with team to design and implement high-throughput experimental workflows for rapid
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an emphasis on technology, data science and the humanities. We are looking for a Research Fellow to conduct AI for medicine research. The role will focus on developing foundation models to medical image
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in the development of regional climate and air quality models, and also the big data analysis. Good communication for stakeholder engagement and writing skills for reports and publications. Evidence of
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, and scientific writing in the fields of ageing, health, and social sciences. The successful candidate will be expected to support the management and data analysis of a large longitudinal study of older
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Studies (SPHS) is a large-scale population-based health research initiative in the Saw Swee Hock School of Public Health, National University of Singapore. This role is vital to the health assessment
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foundational AI and machine learning skills before they enter the full-time programme. Built with cutting-edge technologies such as large language model (LLM) agents, personalized feedback systems, AIAP
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outcomes research work in particular in the area of predictive risk modelling. The position provides the candidate a great opportunity to develop big data predictive analytic skills and work with
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. • Maintain expertise in state-of-the-art data manipulation and statistical analysis. • Strategic and scientific contributions at the indication/protocol/integrated analysis level. • Oversee large scale
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they regulate large protein assemblies involved in mitosis, immune signalling, and protein homeostasis. This role is ideal for someone with strong experimental intuition, precision at the bench, and a strong