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skills, and an excellent command of written and spoken English and Chinese are essential (knowledge of spoken Cantonese would be an advantage). A strong background on quantitative research methods and
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Chinese (Cantonese) and English, advanced knowledge and experience with statistical software (e.g., SPSS, R, Stata) and MS Office applications (including Word, Excel, PowerPoint etc.), and strong team work
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knowledge and/or background in the area. Previous experience in one of the following areas, including assessment and feedback, curriculum design, e-learning, data analysis, and advanced statistical skills
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Responsibilities The objective of this post is to offer interdisciplinary training to individuals interested in utilizing cognitive neuroscience and machine learning techniques to study learning, cognitive
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Bioengineering or Biomedical Engineering, with good experience in biological imaging and image processing. Those with basic knowledge of imaging system development would have an advantage. The appointee should be
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written and spoken English and Chinese (Cantonese); good communication skills; advanced knowledge and experience with statistical and data analysis software (e.g., SPSS, R, Stata, Nvivo) and MS Office
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immunohistochemistry techniques. Working knowledge of SPSS for data analysis. Knowledge of Python and/or R for analyzing next-generation sequencing (NGS) results will be a distinct advantage. Experience in preparing
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scenario, with four specific studies: Develop knowledge representation from multi-modal datasets (tabular EHR data, texts, images) of patients with chronic hepatitis B infection using advanced AI and Large
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discipline. They should demonstrate good knowledge of quantitative and/or qualitative analysis; have a good command of written and spoken English and Chinese (advantage); have good interpersonal skills and a
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: prior technical project management experience and excellent communication skills in English; expertise in Artificial Intelligence based Computational Fluid Dynamics frameworks; knowledge of physics