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invites applications for two positions as Research Assistant Professor (RAP) in Urban Analytics or Construction AI (Ref. no. 532734). Appointments will commence as soon as possible on a one- to three-year
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-on experience in advanced cell culture techniques and in vivo models. Proficiency in various cell and molecular biology experimentation techniques like PCR, gels, Western blotting, Immunohistochemistry, ELISA and
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(e.g., multilevel modelling, structural equation modelling, latent profile analysis, etc.), will be an advantage, as will a strong publication record with internationally recognised journals
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communication abilities. Preference will be given to candidates with research experience in structural biology and molecular biology. In particular, we seek applicants with expertise in techniques relevant
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. Applicants should possess a Ph.D. degree in chromatin biology, epigenetics, structural biology, cell biology, or a related field, and demonstrate exceptional experimental, writing, and communication abilities
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of family and mental health are preferred. They should be competent in advanced quantitative analysis (e.g. SEM, multi-level modelling, factor analysis) and experienced in using statistical software (e.g
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funding availability. Key Duties and Responsibilities Lead and manage innovative research projects in BIM and LLM; Collaboratively develop LLM-based platforms for BIM modeling checking; Assist in crafting
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advanced expertise in developing AI applications using offline/online LLMs (e.g., GPT, Qwen, DeepSeek, Mistral, Llama, Gemma) within Linux environments. They must also demonstrate proficiency in clinical
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Chinese (knowledge of spoken Cantonese would be an advantage). A strong background on quantitative research methods and statistical modeling as well as the design and validation of performance assessment
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statistical modeling as well as the design and validation of performance assessment and psychometric instruments is mandatory. Familiarity with mixed-methods and/or intervention research for youths/vulnerable