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availability and satisfactory performance. Applicants should possess a PhD degree in kinesiology, exercise science, public health or related disciplines. Applicants should have a strong sense of responsibility
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record of publications in relevant fields. Have proficiency in data processing and statistical analysis, with experience in programming languages/software such as MATLAB, Python, R, Stata, as well as GIS
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the field of public health, biostatistics, pharmacoepidemiology and epidemiological studies. They must be proficient in data analysis using statistical or computer modelling software, such as Stata or R
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programming languages/software such as MATLAB, Python, R, Stata, as well as GIS applications (e.g., QGIS). Have excellent communication skills in both English and Chinese, in writing and in oral presentations
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), knowledge of medical statistics and skills of common statistical analysis software. The appointee will be responsible for supervising, coordinating and developing research projects on health and primary care
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the possibility of renewal subject to funding availability and satisfactory performance. Applicants should possess a PhD degree in Biology or related disciplines, preferably with experience in virology research
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of renewal subject to satisfactory performance. Applicants should have a PhD degree in biological/biomedical sciences or a related discipline. They should be hardworking, self-motivated, and able to work
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. Applicants should possess a PhD degree in Biomedical Sciences, Biochemistry, Chemistry or a related discipline. They should have strong background and research experience in at least one of the following areas
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be requested to arrange for at least three references, including one from their PhD supervisor/advisor. Review of applications will commence as soon as possible and continue until November 30, 2025
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collaborative multi-omic research projects relating to gut microbiota and vascular and brain health Involve in the design, implementation, and testing of statistical and AI software for analysing large healthcare