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responsibilities of the position include: - Single-cell and single-nucleus RNA-seq analysis - Spatial transcriptomic data analysis - Designing novel statistical methods for single-cell and spatial transcriptomic
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datasets to address issues related to cancer care and population health. Candidates need to be able to understand statistical modelling, have a strong mathematical background, and be fluent in R programming
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non-market-based conservation measures through data collection, analysis, and dissemination of findings Handle acquisition and processing of spatial data, statistical analyses, report writing and
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, PowerPoint) Basic knowledge of statistics and SPSS. Able to work independently with minimal supervision Able to work cooperatively in teams Proactive, willing to learn, reliable and able to manage concurrent
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include agent based/individual based modelling, SEIR modelling, geospatial statistics, Bayesian statistics, burden mapping, measuring the impact of the environment on disease among others. The PI has
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experts. Methods include agent based/individual based modelling, SEIR modelling, geospatial statistics, Bayesian statistics, burden mapping, measuring the impact of the environment on disease among others
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Job Description The successful candidate will work with Assoc Prof Chua Lay Lay on Synthesis and characterization of electronic-grade organic semiconductors materials, and organic additives for sustainable devices such as flexible polymer battery and polymer solar cell under a project on...
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(socio)linguistics an advantage Familiarity with research on bilingualism, multilingualism and language contact an advantage Experience in using Praat software an advantage Experience in statistical data
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statistics and machine learning More Information Location: Kent Ridge Campus Organization: College of Design and Engineering Department : Industrial Systems Engineering and Management Employee Referral
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, Statistics, Computer Science, Applied Mathematics, or equivalent. Proficiency in statistical software (Python or R) and relevant visualization techniques. Experience in conducting experiments, whether in field