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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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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
Applicants should possess a doctoral or master’s degree in Education, the Learning Sciences or a related discipline, with a strong background in technology-enhanced learning and assessment, learning design and
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proficient in data analysis using statistical or computer modelling software, such as R. They should have an excellent command of written and spoken English and a demonstrated record of publishing academic
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microfluidic technology and/or AI will be an advantage; and (iv) a strong track record of publishing high-quality research articles and grant applications. The appointee is expected to work independently as
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
strong background in technology-enhanced learning and assessment, learning design and/or learning analytics for teacher learning and pedagogical innovation. Familiarity with theory and pedagogical
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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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or computer modelling software, such as R. They should have an excellent command of written and spoken English and a demonstrated record of publishing academic research papers. They should be organized, self
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- School of English - Faculty of Dentistry - Department of Civil Engineering - School of Computing and Data Science - Department of Electrical and Electronic Engineering - Department of Mechanical
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22 Aug 2025 Job Information Organisation/Company THE UNIVERSITY OF HONG KONG Research Field Engineering Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Hong
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infectious disease data using statistical and/or mathematical approaches would be highly desirable. Experience in statistical analysis, and proficiency in statistical and computer modelling software (e.g. R