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to structural biology, protein engineering, machine learning, molecular cloning, in vivo experiments, and/or CRISPR technology. Candidates must exhibit a strong command of written and spoken English, and
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such as machine learning, programming, big data analytics, statistics, social network analysis, natural language processing, and population analysis. The appointee will work on the Master of Social Sciences
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. Requirements A Ph.D. in data science, statistics, psychology, public health, social sciences, or related disciplines. Proficiency in statistical analysis, data mining, predictive modeling, and machine learning
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Experience in image analysis packages such as Freesurfer, FSL, SPM, or 3DSlicer, or using machine learning or artificial intelligence models would be advantageous What We Offer The appointee would be exposed
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and energy materials. Preference will be given to those with knowledge of computer programming, AI or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766
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attached to the socio-cultural environment of the Chinese and English languages. The appointee will be required to teach courses in some of the following areas: Computer-aided Translation, Public Relations
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of English and good communication skills. Preference will be given to those with relevant teaching experience in tertiary institutions, or knowledge of data analytics, machine learning as well as accounting
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closely related data-analytics discipline. The Faculty is particularly interested in those who conduct high-quality scholarly research and are able to teach courses in business analytics, machine learning
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utilising applied economics and statistical methods such as structural modelling, causal inference, or machine learning techniques; and (ii) are open-minded and committed to teaching excellence at both
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advanced digital methods, big data analytics, machine learning, generative AI, large foundation models, embodied and agentic AI to address global challenges in the built environment would be advantageous