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of research include quantum Monte Carlo methods, density matrix renormalization group and tensor network states, and artificial intelligence and neural networks, with particular focus on applying
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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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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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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
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analysis, and proficiency in statistical and computer modelling software (e.g. R, Python, Matlab, and C++) would be advantageous. The appointee will work with a research team to study the methodologies
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independently and as a team. Knowledge and experience in using mixed research methods are highly preferred. A strong publication track record on peer-reviewed academic journals and research grants will be
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adequate knowledge of quantitative research methods, as well as a good command of written English. Experience and passion in research topics of palliative and end-of-life care and public health will be
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for RAP, or a one- to three-year temporary basis for PDF, with the possibility of renewal subject to satisfactory performance and funding availability. Applicants should possess a PhD in epidemiology
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of family and mental health are preferred. They should be competent in advanced quantitative analysis (e.g. SEM, multi-level modelling, factor analysis) and experienced in using statistical software (e.g
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Chinese (knowledge of spoken Cantonese would be an advantage). A strong background on quantitative research methods and statistical modeling as well as the design and validation of performance assessment