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Postdoc: Machine learning for wind flow prediction in coastal dunes Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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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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crop monitoring and decision-support systems for Controlled Environment Agriculture (CEA). This project focuses on the development, validation, and integration of non-destructive plant sensing
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crop monitoring and decision-support systems for Controlled Environment Agriculture (CEA). This project focuses on the development, validation, and integration of non-destructive plant sensing
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-regulatory elements and transcription factor genes that control key physiological pathways to specifically adjust targeted gene expression to reshape complex traits. The project will develop a breakthrough
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vessel Pelagia will allow us to investigate CaCO3 – P interactions in the field. Through controlled lab experiments, we will dive deeper into the underlying chemical and mineralogical processes
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effects on astronauts. Detailed analyses of such effects, and possible ways to shield and mitigate against them, are typically conducted by using environmental prediction in combination with Monte Carlo
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. Anticipating the new legislation, the INPROBED project has been granted, which aims to precisely identify and modify cis-regulatory elements and transcription factor genes that control key physiological pathways
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AI-based decision-making, learning approaches, and predictive capabilities to enable spacecraft to react proactively to events while reducing reliance on ground intervention. A key focus will be
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learning, understand their mathematical foundations, and connect them to space-related technologies and missions. The focus is on building rigorous models that explain and predict the behaviour of modern