Summary

The Di Stefano Lab at Baylor College of Medicine is seeking a motivated Bioinformatics Analyst to join our collaborative research team. This position offers an exciting opportunity to contribute to cutting-edge projects aimed at understanding how post-transcriptional and epigenetic mechanisms regulate stem cell biology, cell fate decisions, and cancer progression.

Our lab uses a wide range of technologies including ribosome profiling, CLIP-seq, ATAC-seq, CUT&Tag, single-cell RNA-seq and multiomics, DNA methylation, RNA splicing, and RNA editing to investigate gene regulation at multiple levels. The successful candidate will support the analysis and integration of large-scale sequencing datasets in close collaboration with experimental scientists and other computational researchers.

This position is ideal for individuals looking to deepen their experience in bioinformatics and data science while working in a highly interdisciplinary and supportive environment.

Job Duties

• Process and analyze high-throughput sequencing data including RNA-seq, ATAC-seq, CUT&Tag, CLIP-seq, and single-cell multiomic datasets.
• Assist with development and execution of computational workflows using R, Python, and command-line tools.
• Work with experimental team members to interpret data and troubleshoot analyses.
• Support manuscript preparation, figure generation, and documentation of analyses.
• Contribute to maintaining organized, reproducible code and workflows.
• Stay informed on best practices and emerging tools in bioinformatics and genomics.

Minimum Qualifications

• Bachelor's degree in Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related field.
• No experience required.

Preferred Qualifications

• Master’s degree in a related field.
• Relevant experience in a computational biology-related field.
• Familiarity with standard tools for RNA-seq and genomic data analysis (e.g., STAR, Salmon, DESeq2, Seurat, ArchR).
• Basic proficiency in R, Python, and Unix/Linux environments.
• Exposure to working with high-performance computing systems and job schedulers (e.g., SLURM).
• Interest in gene regulation, RNA biology, and stem cell biology.
• Strong attention to detail, organizational skills, and ability to work as part of a team.