ADDITIONAL RESPONSIBILITIES: + Analyze large-scale multi-omics datasets including bulk and single-cell RNA sequencing, ATAC-seq, DNA sequencing, proteomics, and metabolomics data from AML patient samples and preclinical models + Perform differential gene expression analysis, pathway enrichment analysis, and network analysis to identify key regulators of therapy resistance + Analyze clonal evolution and lineage tracing data to map resistance trajectories during AML treatment + Integrate functional genomics data (CRISPR screens) with transcriptomic and epigenomic datasets to identify therapeutic vulnerabilities + Develop machine learning models to predict therapeutic response based on multi-omics features + Build predictive algorithms for patient stratification using dynamic BH3 profiling data and molecular profiles + Apply dimensionality reduction techniques and clustering methods to identify AML subpopulations with distinct resistance mechanismsEducation and Experience MINIMUM QUALIFICATIONS: + Bachelor's degree in a scientific field OR equivalent combination of experience, education, and training. PREFERRED QUALIFICATIONS: + Bachelor's degree in Bioinformatics, Computational Biology, Data Science, Computer Science, Statistics, or related field with experience in bioinformatics analysis of genomics data (RNA-seq, scRNA-seq, ATAC-seq, or similar)Technical Skills + Strong programming skills in R and/or Python, Experience with next-generation sequencing data analysis tools and pipelines, Proficiency in statistical analysis and data visualization, Familiarity with high-performance computing environments and cluster job submission systems, Experience with version control systems (Git/GitHub) .