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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
linear regression, mediation analysis, multilevel modeling, and/or latent variable models. Experience in managing and analyzing large datasets, and the use of generative AI tools for research purposes
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of Physics (Ref.: 532531). Applicants should possess a Ph.D. degree in Condensed Matter Physics. Experience in numerical techniques and analytical field-theoretical approaches is desirable. Applicants who
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
innovative methods of assessment and/or advanced statistical methods, such as multiple linear regression, mediation analysis, multilevel modeling, and/or latent variable models. Experience in managing and
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programs, oversee data collection and analysis, prepare reports and presentations, supervise research assistants, and perform other relevant tasks as assigned. There will be many opportunities to collaborate
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experiments involving molecular biology techniques and biostatistical analysis Undertake other duties as assigned 5.5 workday per week is required Requirements a Ph.D. degree, preferably with experience in
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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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of photonics, topological physics, and nanophysics design/refine the research instruments coordinate data collection and analysis support the organization of dissemination events assist in the preparation
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the research instruments coordinate data collection and analysis support the organization of dissemination events assist in the preparation of reports and manuscripts perform other duties as assigned
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well as in a team. Experience in healthcare data analysis and related track records of publishing academic research papers is highly preferred. The appointee will work on healthcare big data related project
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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