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Field
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responsible for adapting existing mathematical and statistical methods for analysis of high-dimensional imaging data, and analysing quantitative imaging data from a variety of sources, including Spatial
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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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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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will also develop skills in mathematical modelling and spatial analysis via independent study and training courses. It is essential that you hold a PhD/DPhil (or close to completion) in mathematics
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, including Spatial Transcriptomics and Multiplex Immunofluorescence platforms, for validation and calibration of mathematical models. You will also develop skills in mathematical modelling and spatial analysis
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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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goal-oriented mindset, and a passion for (scientific) challenges, you are the right person for us. Our lab develops technologies that integrate advanced imaging, computational analysis, and single-cell
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of state-of-the-art bioinformatics pipelines; (ii) single-cell analysis, multi-omic analysis; (iii) spatial transcriptomics bioinformatic analysis. Learning and implementing Systems Medicine-based analysis
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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