GIST Undergraduate Research Opportunities
Undergraduate research opportunities in computational genomics and evolutionary medicine at GIST.
The Computational Genomics & Evolutionary Medicine Lab (PI: Hyeonsoo Jeong) welcomes undergraduate students interested in gaining experience in genomic data analysis and computational biology.
We study genome structure and epigenomic regulation across species and cell types to understand human evolution, aging, and neurological disorders. We are a 100% dry lab and do not conduct wet-lab experiments. Our work analyzes large genomic and multi-omics datasets using statistical modeling, machine learning, and AI to uncover complex biological patterns.
Research opportunities
Depending on their interests and experience, undergraduate researchers can participate in:
- Comparative genomics of humans and other primates
- Analysis of complex genome structure and structural variation
- Epigenomics and gene regulation research
- Single-cell and multi-omics data analysis in aging and disease
- Pattern discovery and prediction in genomic data using machine learning and AI
- Development of bioinformatics tools and reproducible workflows
Students with limited programming or bioinformatics experience begin by reading the literature and learning about the data. They then gradually learn to work in Linux, analyze data with Python or R, create visualizations, and interpret research results.
Students with sufficient programming, statistics, or machine learning experience may contribute directly to data analysis and method development in ongoing projects. Students who make substantive contributions may participate in presentations and manuscript preparation, and may qualify for coauthorship according to their contributions.
Who should apply?
We welcome students who:
- Are interested in genomics, evolution, aging, or disease biology
- Want to analyze real, large-scale biological datasets
- Are interested in the intersection of life sciences with computer science, statistics, or AI
- Want to develop new analytical methods or bioinformatics tools
- Can commit to participating consistently over an agreed period
Programming experience is helpful but not required. Alongside current skills, we value a willingness to learn, consistent effort, and an interest in connecting data with biological questions.
Training
Undergraduate researchers can learn:
- Linux-based computational research
- Data analysis using Python or R
- Processing and interpretation of genomic and epigenomic data
- Biological applications of statistical modeling and machine learning
- Building reproducible analysis workflows
- Reading papers, visualizing results, and presenting research
- Organizing research findings and preparing manuscripts
Regular meetings also provide opportunities to discuss research progress and future career plans.
How to apply
Please email the following information:
- A brief introduction
- Research interests
- Relevant coursework, programming, or research experience
- Your availability and expected duration of participation
- Academic transcript
If you have programming experience, you may optionally include your GitHub profile, analysis code, or materials from previous projects. Applications are accepted on a rolling basis. After reviewing your materials, we will arrange an individual meeting to discuss possible projects and how you can participate.
Email about undergraduate research