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, with the possibility of renewal subject to funding availability and satisfactory performance. Applicants should possess a PhD or Master's degree in Nursing, Social Sciences (including Psychology, Social
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methods. The primary duties of the appointee will include the following: Perform scientific research: (a) to characterize materials, structures and components of Chinese paintings; (b) to broaden ways
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
for technology-enhanced learning and pedagogical innovation is crucial. Preference will be given to those with expertise in innovative methods of assessment and/or advanced statistical methods, such as multiple
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in developing and optimizing synthetic routes, conjugation chemistries, and analytical methods for peptide or protein therapeutics. Excellent communication and collaboration skills, with the ability
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should have sound knowledge of quantitative research methods and a good command of written English. Prior experience and keen interest in research areas such as palliative care, end-of-life care, and
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possess a Ph.D. degree in Social Work, Psychology, Statistics, Public Health, or a related discipline. They should have sound knowledge of quantitative research methods and a good command of written English
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. Requirements A PhD degree with at least 5 years’ post-doctoral work experience in the area of primary immunodeficiencies (PID) and autoimmunity. What We Offer The appointment will commence as soon as possible
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, demonstrate your AI-native capability in any way you think is effective (e.g., a short portfolio of AI-built projects, a small prototype, GitHub repo, etc.). We value signal over formality. Where to apply
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effective (e.g., a short portfolio of AI-built projects, a small prototype, GitHub repo, etc.). We value signal over formality.
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, attentive to details, and able to work independently and in collaboration with a multidisciplinary team. Knowledge of multilevel modelling and other statistical methods for longitudinal data analysis