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. Requirements A PhD in Computer Science or related disciplines. Candidates should have an excellent proven record of research and teaching in Computer Science. Preference will be given to candidates with strong
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be required to undertake certain administrative duties as required by supervisor(s). General Requirements Applicants should hold a PhD degree or an equivalent qualification in Statistics, Data Science
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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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responsibility. Preference will be given to those with interest and potential in pursuing a PhD in Innovation Management. Applicants are invited to contact Prof. Ryan Wang at telephone number 2766 7357 or via
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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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or related disciplines. A Doctoral/PhD degree will be an asset for consideration; Proven ability to teach in English for the courses/domains mentioned in the “Duties” section; Experience in providing academic
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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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teaching and graduate research mentoring. The job duties of successful candidates include teaching at the undergraduate and postgraduate levels, securing research grants, supervising PhD students, and
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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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vibrant and growing team are strongly encouraged to apply. We are looking for candidates who (i) hold a PhD in Marketing or a related field with expertise in quantitative marketing and empirical modelling