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Council (ERC Advanced Grant, 101199790). More information about the project can be found here . We are looking for candidates whose research falls within one or more of the following areas: Plato´s
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digital twins that simulate and predict nitrogen use efficiency (NUE) in forage grasses and oats. By combining high-resolution phenotyping data from the TraitFinder system, drones, and robotic sensors with
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modelling, data-driven climate modelling, and working with large ensembles of climate/weather model output are advantages. The LEAD AI mobility rules must be followed. Outgoing fellowships require
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work upon. Suggested reading to explore this line of research further: Kitto, K., Hicks, B., & Buckingham Shum, S. (2023). Using causal models to bridge the divide between big data and educational theory
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, focussing on communities in Svalbard, Iceland and Greenland. About the project: the project will focus on High Arctic communities’ preparedness to deal with large natural hazard events by leveraging