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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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to develop new carbon capture technologies. We are seeking a motivated postdoc to identify biochemical activities in phytoplankton that convert intracellular metabolites into stable, degradation-resistant
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the following documents: A motivation letter describing your previous research experience Your CV Contact information of at least 2 references For further information and questions, please email Joris de Wit
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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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Polleux, Nat. Rev. Neurosci. 2023). You will explore single-cell epigenomics and transcriptomics data, focused on the neurons of the cerebral cortex from human and an array of non-human species. Using
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Job description We are seeking a postdoc for an interdisciplinary project that integrates high-throughput metabolomics, enzyme assays, and microbiology. Our lab focuses on how microbial interactions
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interested in pushing the boundaries of this emerging research field by zooming in on protein modifications as well. Background information The stomatal lineage represents an ideal model system due
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experience from PhD and/or postdoc research in plant or microbial metabolism, metabolomics, and/or synthetic biology is an asset, as well as a publication record in peer-reviewed journals showing expertise in
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will focus on the development, optimization and application of multi-modal analysis strategies and pipelines for sequencing data generated on nucleic acids isolated from biofluids (genomics, epigenomics
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and/or comparable expertise (e.g from industry). Demonstrated expertise in omics data analysis, for example with (meta)genomics, transcriptomics, proteomics, metabolomics, single cell and/or genotyping