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oncology-specific NLP models with peer-reviewed publications is preferred. Strong quantitative skills are mandatory, including analysing large-scale databases (e.g., Hospital Authority EHR) using R/STATA/SAS
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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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of responsibility and commitment. Preference will be given to those with previous research experience in virus-host interaction, influenza viruses and animal models. Eligibility to work in Biosafety
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Language Models (LLMs). Development and validation of personalized AI models for early diagnostics and risk stratification of progression risks among patients with chronic hepatitis B infection
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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
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, Mechanics, Physics or related disciplines Preferably with research experience in cell and tissue mechanics, theoretical modelling of the collective behavior of cells, and multi-scale simulations. With strong
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and summarize findings related to AI and large language models and their applications to above mentioned fields; Prepare research reports, presentations, and manuscripts for publication; and Collaborate
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Language Models (LLMs). Development and validation of personalized AI models for early diagnostics and risk stratification of progression risks among patients with chronic hepatitis B infection
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computational screening, or machine learning for materials property prediction is essential. Candidates with prior experience in developing AI models for accelerated materials discovery, optimization of material
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. degree in cognitive neuroscience, neural computation and machine learning, or a related field with a strong background in EEG, functional near-infrared spectroscopy (fNIRS), or computational modeling, as