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the appointment. The candidates should have a strong track record in at least one of the group’s research areas, preferably demonstrated by publications in high-impact venues. Experience with 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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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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- “The structural evolution of the Chinese aviation network during and after the pandemic: a machine learning-based approach”. He/she will be required to: (a) conduct high-quality research in the field
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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
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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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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
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Service-Learning and Leadership Office Project Associate / Project Assistant (Ref. 250521001) [Appointment period: six months] Duties The appointee will assist the project leader in the research
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, proactive facility management, building/city health check, and next-generation BIM and digital twins. Having expertise in leveraging advanced digital methods, big data analytics, machine learning, generative