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, machine learning, analysis of unstructured and multimodal data, or advanced quantitative methods and use of AI in communication studies will have an advantage. The appointee will assist in a General
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environment and governance will be highly desirable. Experience in computational social science, causal inference, text mining, machine learning, analysis of unstructured and multimodal data, or advanced
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development initiatives, and fostering industrial and academic partnerships. He/She will teach undergraduate and postgraduate courses in areas, such as AI applications in media and journalism, human computer/AI
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well as experience in applying deep learning and machine learning algorithms for pathogen identification, drug target prediction, antimicrobial drug discovery, and/or protein-protein interaction studies. They should
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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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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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debriefing; developing pedagogical approaches and materials to cater for the learning diversity of multicultural students; promoting learning and teaching (L&T) with assessment data; sharing school-based