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or related disciplines. They should have a strong quantitative background; experience in analysing epidemiological data using R, STATA, SAS or other statistical packages; demonstrated expertise in the analysis
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quantitative background; experience in analysing epidemiological data using R, STATA, SAS or other statistical packages; demonstrated expertise in the analysis of large and complex datasets; excellent written
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
should possess a doctoral degree in Educational Measurement, Psychometrics, Quantitative Psychology, Statistics, or a related field. They should have a deep familiarity with modern test theories, and a
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of renewal subject to satisfactory performance. Applicants should possess a Ph.D. degree in Computer Science, Mathematics, Statistics, computational biology, related disciplines or equivalent. They should be
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
Psychology, Statistics, or a related field. They should have a deep familiarity with modern test theories, and a strong statistical and computing background. The appointee should also have a high level of
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, Statistics, computational biology, related disciplines or equivalent. They should be self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese
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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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value and student learning experience is crucial. Preference will be given to those with experience in qualitative analysis and advanced statistics such as Structural Equation Modelling, Multilevel
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, Neuroscience, Cognitive Science or equivalent, with 3 years’ related work experience, or a Ph.D. degree in a relevant discipline; (ii) solid statistical and computing skills; (iii) effective communication skills
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. Preference will be given to those with expertise in innovative methods of advanced statistics and/or learning analytics, such as multiple linear regression, mediation analysis, mixed-effect models, process