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these conditions. Independently carry out the design and execution of experimental research required by the research component of the project. Independently design, plan, and execute experimental research aligned
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the Principal Investigator (PI) in managing and executing the research project. Conduct literature reviews, data collection, and analysis using multimodal research methodologies. Design and implement experiments
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system. • Design, supervise, and conduct prototyping and experimental campaigns, including setup of instrumentation, data acquisition, and analysis • Document and communicate project progress through
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at the NUS Business School, Department of Analytics and Operations. The research fellow’s primary duty involves conducting research, including model development, theory building, data analysis, and possibly
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methods for multi-omics data analysis. Potential projects may include developing methods for studying tumor microenvironment using spatial transcriptomics data, proteogenomics integrative analysis
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quantitative and/or qualitative research design, data collection and analysis • Have a good track record in leading research projects and stakeholder engagement. Experience in securing research grants and
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. Demonstrated experience in managing large-scale data sets and conducting health-related research studies. Strong analytical skills with proficiency in statistical software and data analysis tools, particularly
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fluorescent microscopy, polarized light microscopy) and biochemical analytical methodologies (e.g. micro-FTIR, micro-XRD, micro-MRI, micro-CT and so on) Bio-sample preparation Literature review, data analysis
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assessment, early health technology assessment, and/or medical innovation development • Health economic modelling with Markov cohort model/ discrete event simulation • Data analysis or data management using
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the financial sector and the economy at large. This role is ideally suited for those wishing to work in academic or industry research in quantitative analysis, particularly in the area of machine learning and