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slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and demonstrated experience in computer vision or analysis of pathology images
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demonstrated experience in computer vision or analysis of pathology images. The appointees will participate in a multidisciplinary collaborative research project related to development of deep learning model
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and Prevention Group of HKU (stroke.hku.hk) to assist in neuroimaging data acquisition and analysis, write-up of the results, as well as training of students Apply image analysis skills on clinical and
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to significantly reduce transport delays, improve material flow, and enhance coordination between logistics and manufacturing processes. c) Digital Twin System: We aim to construct a digital twin framework that
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processes. c) Digital Twin System: We aim to construct a digital twin framework that integrates real-time data and simulation models to mirror the physical manufacturing and logistics systems. This enables
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engineering, computer engineering and applied physics, with a solid academic background in biomedical imaging and AI and an excellent research track record. Candidates should demonstrate a strong academic track
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education, or learning analytics. Familiarity with theories of self- and socially-shared regulated learning, digital literacy frameworks, and/or the role of generative AI in human learning is crucial
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. Applicant(s) should possess a Ph.D. degree, or equivalent, in biological or computer sciences or a related discipline. Experience in any of the following fields, including 1) biological image acquisition
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of self- and socially-shared regulated learning, digital literacy frameworks, and/or the role of generative AI in human learning is crucial. Preference will be given to those with expertise in innovative
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structures, algorithms, and debugging tools; familiar with medical image processing (vtk and ITK), medical device communication protocol (OpenIGTLink) and open-source medical imaging computing platform like