Conduct multi-omics analyses (scRNA-seq, bulk RNA-seq, proteomics, metabolomics) on organoid systems to characterize fidelity and identify biomarkers. Develop computational pipelines, integrate public datasets, and collaborate with experimental teams. Requires a PhD in bioinformatics or related field with strong R/Python skills.
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On-siteEntry levelData Science
About the role
Responsibilities
Analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics data from various organoid systems and their tissue counterparts.
Develop and implement computational pipelines for data processing, quality control, and statistical analysis.
Integrate SOM-generated data with publicly available datasets to benchmark organoid characteristics against normal tissue profiles.
Collaborate with experimental teams to interpret results and guide protocol optimization.
Contribute to manuscript preparation and present findings at scientific conferences.
Requirements
PhD in bioinformatics, computational biology, biostatistics, or a related quantitative field.
Extensive experience with single-cell data analysis, including familiarity with tools such as Seurat, Scanpy, or similar platforms.
Strong programming skills in R and Python.
Experience in statistical analysis and data visualization.
Knowledge of proteomics and metabolomics data analysis workflows.
Nice-to-Haves
Previous experience analyzing organoid datasets.
Experience with machine learning approaches for biological data.
Familiarity with pathway analysis tools.
Knowledge of developmental biology principles.
Experience with high-performance computing environments and version control systems.
Skills
RPythonSeuratScanpySingle-Cell Rna SequencingBulk Rna SequencingProteomics Data AnalysisMetabolomics Data AnalysisStatistical AnalysisData VisualizationMachine LearningHigh-Performance Computing
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