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and psychometric instruments is mandatory. Preference will be given to applicants with knowledge of analyzing quantitative research data using advanced statistical tools (e.g., MPlus, R). Applicants
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data, including data from electronic health records, medical images, and sensor data from wearable devices Responsible for writing up results, training and supervising students and junior staff
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data annotation (brat), Python scripting, and integrating optimization techniques such as fine-tuning, Chain-of-Thought, and Retrieval Augmented Generation to enhance LLM outputs. Experience in creating
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technical skills in next generation sequencing, including library preparation and bioinformatics analysis. Experience in developing bioinformatics pipeline and maintaining computer server would be
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a Ph.D. degree in Health Science, Sport Science, Psychology, Public Health or Gerontology preferably with experience in coordinating research projects, data management, and research dissemination
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or related disciplines. They should have i) a strong quantitative background; ii) experience in analysing epidemiological data using R, STATA, SAS or other statistical packages; iii) demonstrated expertise in
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assist the Principal Investigator in managing research projects and be responsible for literature review, data collection, data analysis, preparation of presentations, drafting proposal and reports, and
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, fluent in written and spoken English, and competent in basic computer database management. Familiarity or experience with gene sequencing or gene manipulation will be an advantage. The appointee will be
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informed neural networks (PINN) and explainable machine learning (EML) frameworks; experience in related technologies including large-scale data analysis, deep learning, Python, PyTorch; and the ability
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groups/minorities would be an added strength. Preference will be given to applicants with knowledge of analyzing quantitative research data using advanced statistical tools (e.g., MPlus, R). Applicants