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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
, and Fagales. Research Focus While the two positions will work closely together and share many responsibilities, they have slightly different initial focus areas: Position 1: Computational Genomics and
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Job description We’re looking for a postdoc with hands-on experience in genetically modifying marine phytoplankton—someone excited to learn how microbes naturally sequester carbon in the environment
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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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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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scientist to study the human-specific genetic mechanisms of development and function of cortical neurons. Join us to undertake a highly interdisciplinary project focusing on a long-standing and fascinating
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, Statistics, Computer Science, Genetics, or equivalent. Proficiency in Python and/or R, with a solid grasp of statistics. Excellent organizational and time management skills, with a flexible
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brain connectivity, as defects in microglia have been recently linked to neurodevelopmental disorders. In addition, genetics of neurodegeneration place microglia as one of the central drivers of disease
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brain connectivity, as defects in microglia have been recently linked to neurodevelopmental disorders. In addition, genetics of neurodegeneration place microglia as one of the central drivers of disease
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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