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algorithms, NLP models, and LLMs to analyze complex data. Designs and implements novel data science methodologies for predictive modeling, causal inference, and probabilistic analysis in clinical and
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune
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The successful candidate will be responsible for developing computational and systems biology approaches to analyze spatial omics data and single-cell omics data (e.g., scRNA-seq, scATAC-seq, single
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. The most recent methodological research of the group includes algorithms for cell type deconvolution, high-resolution purification, and integration of single cell multi-omics data. This postdoctoral fellow