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tissue samples. The ideal candidate will have prior experience in the analysis of single-cell transcriptomic datasets and is eager to learn and develop new spatial transcriptomics approach
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processing, imaging, and machine learning to analyze data and help with aggregation, harmonization, and dissemination of our datasets to the research community. More specifically, they will be able
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in bioinformatics) Background and work knowledge in scientific computing, algorithms, and machine learning or statistics is required Familiarity with R or Python, and the Unix (Linux) environment is
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