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, (3) the development of translational strategies in circuit neuroscience and (4) the development of computational models and methods for neuroscience. The goal is to implement AI-based methods and
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-scale screens to study fundamental principles in molecular and complex trait genetics using microbes as model systems. Our core technology MAGESTIC (https://doi.org/10.1038/nbt.4137 ), a CRISPR/Cas9-based
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training data. You will unravel the cis-regulatory code controlling context-dependent gene expression and use this information to design synthetic promoters. You will train and evaluate predictive models in
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critical roles of ion channels—particularly the TRP superfamily—in physiological and pathological processes. Our interdisciplinary approach spans from foundational electrophysiology to translational models
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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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question: « what makes our brain human ? » (Vanderhaeghen and Polleux, Nat. Rev. Neurosci. 2023). We combine cutting-edge approaches such as pluripotent stem cell models of human corticogenesis, human-mouse
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of cellular metabolism or physiology. Experience in genetic engineering of phytoplankton or mass spectrometry-based metabolomics is a plus. The postdoc will get training in high-throughput metabolomics and
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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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metastasis and novel metabolic pathways. We exploit mouse models, genetic engineering, metabolomics and single cell & spatial multi-omics analysis to gain groundbreaking insights into metabolism as a driving
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project that combines cutting-edge approaches including state-of-the-art imaging techniques, pluripotent stem cell models, in vivo mouse models of neurological disorders, drug (brain) delivery and nanobody