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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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: 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
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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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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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on computational and theoretical condensed matter physics. Areas of research include diagrammatic calculations, quantum Monte Carlo methods, density matrix renormalization group and tensor network states, and
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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computational methods to address and derive theoretical models and predictions. For this line of research we are seeking several postdoctoral researchers to work synergistically both within the team and with our
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Ph.D. degree in Epidemiology, Biomedical Sciences, Computer Science, Data Science, or related disciplines. A proven track record in peer-reviewed publications and research experience in computer graphics
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methods, protocols, and reports and provide training on processes and equipment. Supervise and co-supervise undergraduate and postgraduate students. Disseminate research findings through the preparation