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of complex proteomics data derived by mass-spectrometry. Your job In this PhD project, you will develop AI-driven methodologies for analysing a variety of mass spectrometry data, including metabolites
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microbiome research. In this position you will focus on advanced data analysis of data gathered by e.g. genome sequencing, RNAseq, proteomics and plant metabolomics, to help develop and safeguard (novel) tools
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peer-reviewed papers annually). The laboratory hosts state-of-the-art analytical equipment for proteomics, metabolomics and glycomics, and multiple targeted diagnostic platforms to translate research
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microbiome research. In this position you will focus on advanced data analysis of data gathered by e.g. genome sequencing, RNAseq, proteomics and plant metabolomics, to help develop and safeguard (novel) tools
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-resolution mass spectrometry-based proteomics. In addition you will perform functional in vitro assays, such as assessment of macrophage function and mitochondrial activity in sensory neurons to assess
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research groups and many excellent facilities, including for microscopy, flow cytometry, protein expression and purification, genomics, proteomics and many more. The laboratory is part of the Division
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on molecular pathways encompassing (e.g. genomics, proteomics and metabolomics) is needed. Currently, this is not simple nor scalable. There is an increasing need to combine -omics data from different sources
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many functional neutrophil assays available will be combined in this project with plasma proteomics to elucidate the possible key factors involved. The ultimate goal is to find means to inhibit NET
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: Experience with RNA biology and associated methods (i.e. PAR-CLIP, m6A profiling, etc). Experience with mammalian cell culturing. Experience with bioinformatics analysis of large (proteomics, genomics, and
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. The aim of the project is to characterize the spatio-temporal changes in plasma membrane proteome during the response to environmental stimuli focusing on the root of the model organism Arabidopsis thaliana