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deep learning models with state-of-the-art algorithms based on histology whole slide images. They may also contribute to research of integration of multimodality data with clinical, radiological images
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, School of Clinical Medicine, is seeking a highly motivated and talented researcher to join our multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on
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computational approaches, including algorithm-guided design, sequence and structure-based analysis, epitope-related studies, and functional evaluation to support antibody development and mechanistic investigation
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highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities
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for diagnosis and prognosis of different sarcomas. He/she will develop and train deep learning models with state-of-the-art algorithms based on histology whole slide images. They may also contribute to research
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the possibility of renewal subject to satisfactory performance. Applicants should have a Ph.D. in bioinformatics, genetics, computational biology or other relevant disciplines. Experience in cancer research and/or
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of renewal subject to funding availability and satisfactory performance) Applicants must hold a Ph.D. degree, preferably in developmental biology, animal genetics, molecular biology, or a closely related
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a strong track record in urology research, in particular on genetic epidemiology, genetic functional study, and clinical translational research for prostate cancer and other urological diseases
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; (ii) a strong track record in breast surgery research, in particular on molecular genetics, next generation sequencing, cancer biology, tumour microenvironment, and clinical translational research