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). The RAs will help us to improve an existing Python code base for creating 3D models of ancient artifacts from large datasets of 2D images. The main qualifications are prior experience working on large
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, cell-laden and multicellular 3D bioprinting, vascularized tissue-model construction, cell culture, and biological and mechanical characterization of engineered constructs. Experience in rheology
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self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in AI deep learning model development on histology whole
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Responsibilities Participate in research program of PID modelling using stem cell platforms conducted in the Division of Rheumatology & Clinical Immunology. Supervise junior research staff and postgraduate students
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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 to ample
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, attentive to details, and able to work independently and in collaboration with a multidisciplinary team. Knowledge of multilevel modelling and other statistical methods for longitudinal data analysis
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responses. Structural Bioinformatics & Biophysical Modeling: Protein structure prediction (e.g., AlphaFold, RoseTTAFold, ESMFold), molecular dynamics (MD) simulations, or protein-protein/antigen-antibody
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(e.g., scRNA-seq) to investigate cellular heterogeneity, host-pathogen interactions, and immune responses. Structural Bioinformatics & Biophysical Modeling: Protein structure prediction (e.g., AlphaFold
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of public health, biostatistics, pharmacoepidemiology and epidemiological studies. They must be proficient in data analysis using statistical or computer modelling software, such as Stata or R. They should
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multimodality deep learning model development in different sarcomas. Publications in related fields will be a strong advantage. Opportunities for publication and independent development will be available