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the development of artificial systems that each integrate different aspects of machine learning, multimodal sensing, ubiquitous computing and social science. In addition, the successful candidates will have the
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to computational cardiology. In this project, you will combine advanced physics-based models of the human heart and vasculature with the latest breakthroughs in machine learning to develop scalable and robust
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the content we engage with, the products we purchase, and our social interactions. These systems, driven by machine learning, support a wide range of human decision-making processes across e-commerce, social
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for a motivated candidate to work on the topics of theoretical machine learning, specifically in the domain of sequential decision making, which includes bandit problems and theoretical reinforcement
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into account adequately in models. Therefore, we will work with traditional parameterizations for cloud microphysics, but also explore how physics-enhanced machine learning can help us to develop new ways
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networks to reduce the impact of network failures or epidemics; prevent viral epidemics in human population. Epidemic processes widely apply to biological and computer network viruses, to cascading failures