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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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within the Faculty. Along with the disciplinary approach a very ambitious interdisciplinary research culture has been developed. The faculty's research and teaching focuses on social, economic, political
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4 fully funded PhD student positions in the computational, mathematical & experimental plant science
. More than 30 research groups study regulatory networks controlling fundamental traits such as reproductive development, organ geometry and growth, innate immunity and microbiome effects on plant performance
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