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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
innovative methods of assessment and/or advanced statistical methods, such as multiple linear regression, mediation analysis, multilevel modeling, and/or latent variable models. Experience in managing and
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Stephanie Ma on research projects relating to cancer cell plasticity with a focus on cancer stemness using hepatocellular carcinoma as a model system, that is part of a theme-based collaborative project
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satisfactory performance and funding availability. Applicants should possess a PhD degree in epidemiology, biostatistics, applied mathematics, data science or other related disciplines. They should have strong
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as soon as possible for one or two years on temporary term basis, with the possibility of renewal subject to satisfactory performance and funding availability. Applicants should possess a PhD degree in
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optimizing high-performance C++/C# software modules for real-time control, sensor fusion, and data analysis; developing unity‐based visualization and user-interaction interfaces, including 3D modelling
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immunology and molecular biology techniques, and able to work independently and in a team. Preference will be given to those with work experience in transcriptome, animal models and molecular experiments
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‐based visualization and user-interaction interfaces, including 3D modelling, physics integration, and cross-platform deployment (PC, mobile, AR); integrating and deploying AR navigation applications
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
organizational skills and the ability to work independently and collaboratively within a team on a tight schedule are essential to succeed in this role. Applicants in the final stages of their PhD studies may be
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an outstanding research track record and extensive expertise in cancer bioinformatics, cancer biology, cancer immunology, deep learning models and/or artificial intelligence, as well as hands-on experience with
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immunology, deep learning models and/or artificial intelligence, as well as hands-on experience with cell culture, cellular/molecular biology, and animal studies. The ideal candidate should be self-motivated