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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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computational and machine learning approaches, you will decipher genomic regulatory programs and infer the evolutionary patterns of gene regulatory networks in cortical neurons, study their developmental origin
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and stress conditions by combining single-cell genomics, artificial intelligence, and synthetic biology. Apart from shedding light on the fundamental aspects of transcriptional control, this project
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record in exploring urology‑relevant sensory mechanisms, with translational relevance for conditions such as chronic pain, bladder dysfunction, and other urinary tract disorders. The urinary bladder plays
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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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to unravel how plants control gene expression across different tissues and stress conditions by combining single-cell genomics, artificial intelligence, and synthetic biology. Apart from shedding light on