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collaboratively. The appointee will supervise and manage large-scale randomized controlled trials investigating the effects of diverse combinations of exercise prescription on prevalent chronic diseases, including
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to multitask and work independently as well as in a team. Experience in large-scale research project management and development, knowledge of statistics and big data analysis would be highly desirable. A highly
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Chinese Able to multi-task and work independently as well as in a team Experience in clinical research is perferable What We Offer The appointee would be exposed to ample opportunities in developing
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oncology-specific NLP models with peer-reviewed publications is preferred. Strong quantitative skills are mandatory, including analysing large-scale databases (e.g., Hospital Authority EHR) using R/STATA/SAS
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culture, cellular/molecular biology, flow cytometry and animal studies. Applicants should be self-motivated, organized and able to work independently as well as in a team; and have the ability to multi-task
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learning strategies; work independently in developing, implementing and evaluating school-based support plans for project schools; nurture project schools’ multi-level innovation and leadership capacity
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institutes/centres. There are around 40,000 undergraduate and postgraduate students who are recruited globally, and more than 4,000 members of academic and academic-related staff coming from multi-cultural
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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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, animal studies, and cancer-related research work. Applicants should have good communication skills, be self-motivated, have the ability to multi-task with attention to detail, be organized and be able
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ethical approval application, consent form and information sheet preparation, instrument development and validation, scale development, data cleaning and analysis, and report writing. Support the PI in