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in the research project - “Towards efficient stamp forming of thermoplastic composites through data-driven thermo-mechanical modelling”. Qualifications Applicants should have a doctoral degree or an
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the research project - “Towards efficient stamp forming of thermoplastic composites through data-driven thermo-mechanical modelling”. Qualifications Applicants should have a Master’s degree or a good honours
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magnetic resonance imaging (MRI), such as arterial spin labeling and diffusion tractography using programming scripts, such as Python and C++ Self-driven, motivated, and creative in designing image analysis
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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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methodologies for biological and biomedical discovery • Advanced computational approaches for large-scale multi‑omics data, biological imaging, and systems biology • Predictive modelling and data-driven
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of renewal subject to satisfactory performance. Applicants should possess a Ph.D. degree in biological science, biomedical sciences, bioinformatics or related disciplines. They should be self-driven, highly
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with cutting-edge computational and AI-driven methodologies, contributing to the Department's strategic vision of integrating geospatial data sciences with environmental and sustainability research
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supervision. Candidates with 2-3 years of postdoctoral experience are preferred. Experience with AI-driven Earth observation data analysis, physically based modelling, ecological and carbon cycle research
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-driven Earth observation data analysis, physically based modelling, ecological and carbon cycle research, public health research, or environmental monitoring systems will be considered an advantage. Terms
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a new interdisciplinary school at HKU dedicated to preparing students to create meaningful impact in a rapidly changing, technology-driven world. With its undergraduate and postgraduate programmes in