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Field
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managing diabetes using computer simulation models. Learning Objectives: You will learn: How to synthesize and translate empirical evidence on cost-effectiveness of interventions for the prevention and
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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Science and Engineering, or a related discipline Research experience in optical imaging, structured-light three-dimensional reconstruction, computational imaging, computer vision, machine learning
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the following will be a plus: • Optical instrumentation, single-molecule/ super-resolution microscopy, magnetic/optical tweezers, ultrafast spectroscopy, or other in-situ operando imaging tools • Machine
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the following will be a plus • Optical instrumentation, single-molecule/ super-resolution microscopy, magnetic/optical tweezers, ultrafast spectroscopy, or other in-situ operando imaging tools • Machine
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 3 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an exciting place to learn and
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denoising, cell segmentation and the analysis of cellular neighbourhoods and cell–cell interactions. Experience in developing artificial intelligence and machine-learning methodologies for multimodal data
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measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn
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many areas of applied and theoretical statistics and data science, and is heavily involved in research at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to