Key Responsibilities: • Analyze and integrate high-dimensional chemoproteomics datasets with multi-omotic data (e.g., genomics, transcriptomics, proteomics) from public repositories such as DepMap and TCGA • Develop, implement, and maintain robust computational pipelines in R and other programming languages (e.g., Python) for data processing, statistical analysis, and visualization • Mine and interpret large-scale CRISPR screening datasets to identify novel cancer dependencies and therapeutic targets, utilizing resources such as DepMap and published CRISPR screens • Collaborate with interdisciplinary teams of biologists, chemists, and clinicians to design and execute integrative studies, and contribute to the functional validation of computational predictions • Present research findings at internal meetings and national/international conferences; publish results in high-impact journals • Mentor junior researchers and contribute to grant writing and manuscript preparation Required Qualifications: • PhD in Bioinformatics, Computational Biology, Cancer Biology, Genomics, or a related field is required • Demonstrated expertise in R and proficiency in other programming languages (e.g., Python) • Experience working with and mining large public cancer genomics databases, such as DepMap and TCGA • Proven track record in analyzing and integrating chemoproteomics and/or functional genomics datasets, especially CRISPR screening data • Strong understanding of statistics and machine learning concepts as applied to biological data • Excellent written and verbal communication skills, with a history of publishing in peer-reviewed journals • Ability to work independently and collaboratively in a multidisciplinary research environment Preferred Qualifications • Experience with drug development and/or chemistry • Familiarity with AI/ML approaches for biological data analysis. The successful candidate will participate in research projects leveraging large-scale chemoproteomics datasets, integrating these with public cancer genomics resources, and mining CRISPR screening data to advance cancer biology and therapeutic discovery.