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Field
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Postdoctoral researcher in the analysis of single-cell and spatial transcriptomics experiments (M/F)
progression. The selected candidate will perform single cell and spatial transcriptomic analysis. - Cell preparation for transcriptomic analysis. - Data mining using pipelines developed by the laboratory
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analysis techniques, including space syntax, isovist measures, and visual complexity assessments. The successful candidate will work closely with researchers at Cambridge and ETH Zurich to quantify spatial
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the analysis of multimodal spatial omics data across multiple projects, collaborating closely with experimental biologists. Main tasks and responsibilities: Develop and implement robust computational pipelines
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computational biology and mathematical spatial analysis via independent study and training courses. It is essential that you hold a PhD/DPhil (or close to completion) in mathematics, computational biology, data
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hands-on experience in tissue handling, histology, microscopy, and spatial transcriptomics. Experience with the computational analysis of NGS- or imaging-based spatial transcriptomics and single-cell
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for identification of discriminative spatial interactions of therapeutic response, and develop skills in computational biology and mathematical spatial analysis via independent study and training courses. It is
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of statistical packages (Stata, R or equivalent). • Experience in spatial analysis and use of Geographic Information Systems (GIS). • Ability to work with large databases and data management tools. • English
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skills (one or more of the following strongly desired) Exploratory analysis of massive datasets (machine learning methods) Spatial data analysis and Geographic Information Systems (GIS) Forecasting and
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in providing computational methods to improve the representation, retrieval, integration, analysis, visualization, and communication of spatial and spatiotemporal data with a wide range of applications
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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human