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also opens new avenues for the design of climate-resilient crops. You will apply AI strategies to learn the regulatory syntax encoded by the Arabidopsis genome using single-cell transcript data as
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and ultimately accelerate these activities to combat climate change. Our approach combines science and engineering, beginning with the study of fundamental microbial metabolic processes that can be
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and ultimately accelerate these activities to combat climate change. Our approach combines science and engineering, beginning with the study of fundamental microbial metabolic processes that can be
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the fundamental aspects of transcriptional control, this project also opens new avenues for the design of climate-resilient crops. Supported by single-cell profiling and predictive artificial intelligence models
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working on a third-party funded project funded by the Luxembourg National Research Fund (FNR) on 'Rule of Law Principles - which model for the Global South?' The project focusses on the transition
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temperature signalling in plants, such as the model plant Arabidopsis thaliana and the crop plants wheat and soybean. To unravel this, we focus on dynamic changes in protein phosphorylation status, since
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-climatic zones, as a cost-effective and practical way for long-term monitoring and to support various approaches for N flux modeling and analysis. Ghent University (UGent) is the coordination partner