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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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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
Integration of phenotypic data with omics analysis Explore machine learning and network analysis methods Profile Essential A PhD in Bioinformatics, Computational Biology, Evolutionary Biology
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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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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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significant potential for applications in agriculture and human health. However, many remain undiscovered, as their biosynthesis is often inactive under laboratory conditions. Our goal is to uncover these novel
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, CRISPR/Cas9 gene editing, viral vectors, conditional knockout mice, optogenetics, and in vivo imaging. The successful candidate will be based in Leuven, Belgium. The two labs will interact regularly via
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) acquire a preparatory post-doctoral research mandate to further develop and submit a competitive research proposal to FWO (Flemish Science Foundation) or MSCA (Horizon Europe Marie Curie fellowships), with