Research Assistant Professor-Genomic Sequencing Data Analysis

Research Assistant Professor-Genomic Sequencing Data Analysis We are seeking a highly skilled and motivated Research Assistant Professor with expertise in genomic sequencing data analysis to join our multidisciplinary research team. The successful candidate will lead computational and statistical analyses of large-scale genomic datasets, including whole-genome, whole-exome, and transcriptomic sequencing, to advance projects in cancer biology, precision medicine, and related biomedical fields. This position offers the opportunity to develop independent research while contributing to collaborative team science. Responsibilities Perform high-quality computational analysis of next‑generation sequencing (NGS) data, including short and long‑read whole‑genome, whole‑exome, RNA‑seq, and spatial transcriptomics datasets. Develop and implement bioinformatics pipelines for variant calling, structural variant detection, transcriptome profiling, and integrative multi‑omics analyses. Apply statistical and machine‑learning approaches to identify genomic alterations, biomarkers, and functional networks. Collaborate with wet‑lab scientists to integrate genomic data with experimental results. Contribute to manuscript preparation, figure generation, and presentation of findings at scientific conferences. Write and contribute to competitive grant applications, providing preliminary data and computational expertise. Mentor graduate students, postdoctoral fellows, and research staff in computational genomics. Maintain data management, quality control, and reproducibility standards in accordance with institutional and funding agency guidelines. Preferred Qualifications Ph.D. or equivalent degree, with postdoctoral training in bioinformatics, computational biology, genomics, computer science, statistics, or related field. Demonstrated expertise in NGS data analysis, including quality control, alignment, variant calling, and downstream interpretation. Proficiency with bioinformatics tools (e.g., GATK, samtools, bcftools, STAR, HISAT2, Cell Ranger, Seurat) and programming languages (e.g., Python, R, Bash). Experience working with high‑performance computing and cloud‑based analysis platforms. Experience with cancer genomics, single‑cell and spatial transcriptomics, or epigenomic data analysis. Additional Information Travel is required; overnight, weekend, and after‑hour work may be necessary. The position will be supported using grants or contract funding. #J-18808-Ljbffr