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computational colleagues to build, train, and evaluate cutting edge AI models using large proprietary oncology datasets Leverage multimodal high dimensional data to investigate relationship between heterogeneous
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. Scientific insights resulting from this research are expected to be presented at scientific conferences and published in high-impact journals. The Opportunity: Generate new methods for large-scale spatial
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statistical genetics Proven experience with applying standard pipelines for the analysis of large-scale genetic data sets Demonstrated proficiency with R or Python Demonstrated ability to effectively
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at the intersection of single-cell omics, ML/AI and target discovery. The successful candidate will lead the generation of large scale perturbation datasets with multimodal readouts in complex human models and use