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microbiome and genome data to learn contextual representations of microbes and communities and translate them into predictive models for successful crop microbiome engineering. Your job Plant-associated
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are you going to do? The project addresses a central question in mechanobiology: how do cells sense, encode and respond to mechanical cues? You will develop a quantitative and predictive framework
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Develop advanced models to understand and predict piping
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at experimentally informed microstructural features. You will combine first-principles modelling and machine-learning approaches to develop predictive simulations of hydrogen behaviour in compositionally complex Fe
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will contribute to a scientifically challenging and industrially relevant topic, with the aiming at predictive relations between measurable wafer surface properties and bonding performance for next
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technologically relevant problem. Project goal The project aims to develop a predictive, experimentally grounded understanding of how rapid solidification and ambient pressure shape molten-tin droplet impacts, and
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and review the wide range of applications of IoT sensors, their uncertainties, and the observable variables above and below ground that are important for predicting tree crop diseases and stresses. You
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can be accommodated within a general foundation model framework, and to what extent a common model can generalize across them. Research directions may also include generative or predictive modeling
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Professors with ambition to grow who will help shape the future of phytopathology through innovative research on disease resilience, pathogen adaptation, quantitative disease biology, predictive modelling
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expand and improve an existing modelling framework to predict direct and indirect nitrous oxide and methane emissions from agriculture. You contribute to the following activities: Performing a SWOT