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approaches that predict the static structure of proteins have become invaluable tools for biologists and medicinal chemists, machine learning-based prediction of protein dynamics is still in its infancy
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fulfil its biological function. While machine learning approaches that predict the static structure of proteins have become invaluable tools for biologists and medicinal chemists, machine learning-based
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diseases, and how these influence, or are influenced by, labor force participation and income. In addition, you will develop simulation models to predict how different policies could reduce the disease
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. Thus, understanding the future viability of ice shelves in a warming climate is paramount to better predict the future sea level rise. The state and evolution of the firn layer, expressed in
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project focuses on developing methods to assess the sustainability of nearly 100 nationwide AI systems within the National Lab on Education and AI (NOLAI). You will create methods to predict energy
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, ultimately, predictable by machine learning. Specifically, you will build a first-in-class framework to expedite the design of high-affinity binders that engage with therapeutic targets or efficient (bio
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groups. Interdisciplinary focus: This project links Disciplines: comparative biology, optics and biophysics. Theory: optical theory provides us with specific predictions as to what eye designs would work
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. An aortic aneurysm is a bulge in the body’s main artery, the aorta. The bulge can rupture, unfortunately leading to death in more than eighty per cent of cases. It is not currently possible to predict whether
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for predictive maintenance of an operating AEM cell developed by collaboration partner HyET E-Trol. A core feature of your work should be the inclusion of a neural network (NN) surrogate of a detailed physics
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theory provides us with specific predictions as to what eye designs would work best for specific visual tasks. Biological theory provides predictions about energy efficiency and behaviour. Fundamental and