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placed on the role, the design and the functioning of the so-called agroparks – spatial ensembles in which different aspects of the food chain come together: indoor and outdoor cultivating centers with
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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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, or dynamic models to predict gene regulatory interactions. Work with digital twin technology, simulating patient-specific disease progression and treatment responses. Collaborate in an interdisciplinary
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on Education and AI (NOLAI). You will create methods to predict energy consumption, create energy labels for algorithm scalability, and guide implementers in choosing more efficient algorithms. Ready to make AI
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, perform experiments, and develop prediction models. You will focus on the effect of deconstruction on the structural strength, stability, and fatigue life of reused components. Your goal is to develop a set
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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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. 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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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