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communication skills in both English and Chinese; (d) have strong empirical research skills with expertise in causal inference methods (e.g., quasi-natural experiments, field experiments); (e) be proficient in big data
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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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laboratory management, training, and method development Requirements PhD in Earth Sciences, Chemistry, or a related field Hands-on experience with MC-ICP-MS, TIMS, and nitrogen and noble gas analysis
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social sciences research methods Applicants should indicate which mentors from FOSS (HKU) and ISR (Michigan) they could work with. There is no need to get their interest and support in mentoring before
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an evidence-based outcome measurement framework for the project; Conduct comprehensive impact measurement research on the project with both qualitative and quantitative methods; Strengthen and refine key
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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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of research include diagrammatic calculations, quantum Monte Carlo methods, density matrix renormalization group and tensor network states, and artificial intelligence and neural networks, with a particular
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computational methods to analyse multi-omics data, specifically focusing on ribosome profiling, mass spectrometry-based proteomics, next-generation sequencing (NGS) and spatial transcriptomics, etc.; (c
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to formulate mathematical models of the problems and develop efficient solution methods, particularly by leveraging techniques from machine learning and operations research. b) The applicants are expected
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: Contribute to the design of an evidence-based outcome measurement framework for the project; Conduct comprehensive impact measurement research on the project with both qualitative and quantitative methods