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the statistical analysis of neural data We offer A stimulating, international, interdisciplinary, collaborative, and supportive work environment, which emphasizes diversity and inclusion
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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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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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Excellent communication skills in English Desirable but not required Skills in phytoplankton genetic engineering Hands-on experience with high resolution mass spectrometers and/or data analysis Programming
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spectral flow cytometry and microscopy (FELASA certificate required). Experience with -omic approaches and computational tools for data analysis is desirable. Solid publication record in peer-reviewed
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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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questions you addressed in your PhD. Your CV Contact information of at least 2 references For questions or inquiries; please contact Sammy Pontrelli, Sammy.pontrelli@vib.be.
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-mediated perturbation approaches have solid knowledge of and experience in single-cell data analysis and multimodal dataintegration have strong knowledge of gene regulatory mechanisms have basic/intermediate
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computing clusters and analysis of transcriptomics and genomics datasets. Desirable Requirements Experience in single-cell and spatial OMICS data analysis. Development of ShinyApps and
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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