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with cell culture techniques. R, Python, or other data science tools for microbiome analysis. Publication record in peer-reviewed journals. Preferred Qualifications Experience with colorectal cancer
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-cell microbiome analysis, or multi-omics data integration. Knowledge of machine learning approaches for resistome prediction or biomarker discovery is a plus. Why Join Us? Access to cutting-edge
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sequencing (NGS) and omics data analysis. Knowledge of microbial ecology, dysbiosis, and host-microbiome interactions. Familiarity with cell culture techniques. R, Python, or other data science tools
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to temperature changes through stress priming and memory in the early growth phases. This project is part of a strategic initiative to develop temperature-resilient crops by harnessing the mechanisms of plant cell
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research, oncology microbiomes, or environmental resistome surveillance. Familiarity with spatial metagenomics, single-cell microbiome analysis, or multi-omics data integration. Knowledge of machine learning