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motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in bioinformatics, deep learning and/or biomedical image and clinical data analysis
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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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application of advanced machine learning techniques, including deep learning approaches, aimed at identifying complex patterns of host immune responses to vaccination. The research activities will also include
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do things, especially considering recent advancements in AI technology. The position will include developing radiomics and deep learning models from contrast-enhanced computed tomography images
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multisensor fusion and ondevice AI pipelines that guarantee tight latency, power efficiency, and fail-safe robustness. Driving hardware–software codesign to radically optimize state estimation and deep-learning
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monitoring agricultural emissions across Africa using satellite remote sensing, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH3, N2O, CO2, CH4) using satellite
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equivalent qualification with one to two years of advanced research experience in generative AI, visual computing and deep learning, and must have no more than five years of post-qualification experience
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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candidate will contribute to projects in one or more of the following areas: influenza transmission and epidemic outbreaks, including developing advanced deep learning models to predict emerging antigenic and
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modern deep learning frameworks (e.g., PyTorch) and evaluate them with a focus on interpretability, robustness, and real-world applicability in healthcare settings. The role also involves developing