Graduate Research Opportunities

M.S.-Ph.D. integrated and Ph.D. opportunities in computational genomics and evolutionary medicine at GIST.

The Computational Genomics & Evolutionary Medicine Lab (PI: Hyeonsoo Jeong) is recruiting students for the integrated M.S.-Ph.D. and Ph.D. programs in the Department of Life Sciences at GIST.

We study how genome structure and epigenomic regulation contribute to human evolution, aging, and disease. As a 100% dry lab, we analyze large genomic and epigenomic datasets, including long-read sequencing and single-cell multi-omics, and compare them across species. Our research uses statistical modeling, machine learning, and AI, alongside the development of new computational methods and bioinformatics tools.

Research directions

1. Genome structure and primate genome evolution

We use long-read sequencing and telomere-to-telomere genome assemblies to study complex regions that are difficult to analyze with conventional reference genomes. We investigate how segmental duplications, structural variants, centromeres, and repetitive sequences differ among individuals and species, and how these differences relate to human evolution and disease.

Example topics: population-scale structural variation, human pangenome, ape genome comparison, duplicated gene evolution, complex genomic region annotation

Representative papers: Jeong et al., Nature Genetics, 2025; Yoo et al., Nature, 2025; Makova et al., Nature, 2024; Mao et al., Cell, 2024.

2. Comparative epigenomics and human-specific gene regulation

We compare cell-type-resolved epigenomes from humans and non-human primates to study regulatory changes in the human lineage. By analyzing DNA methylation together with gene expression, we examine how these changes relate to brain development and function, and to gene regulation associated with neuropsychiatric disorders.

Example topics: human-specific regulatory elements, cell-type-specific epigenomic evolution, comparative brain genomics, neurological disease-associated regulatory variation

Representative papers: Jeong et al., Nature Communications, 2021; Mendizabal et al., Genome Biology, 2019.

3. Single-cell epigenomics of aging and disease

We integrate single-cell DNA methylation, chromatin accessibility, transcriptomic, and 3D genome data to study aging in different cell types. We investigate how cell identity and gene regulation change during aging and disease, and identify epigenetic aging signatures shared across tissues and species.

Example topics: epigenetic aging, cell identity loss, chronic disease-associated repair states, cross-species aging, single-cell multi-omics integration

Representative papers: Jeong et al., Nature Aging, 2026; Jeong et al., GeroScience, 2024.

4. Machine learning and bioinformatics method development

We develop computational methods to quantify structural variation, cell-type-specific regulatory changes, and differences across species in complex genomic and epigenomic data. We also develop lineage-tracing methods that infer cell lineages and clonal structure from single-cell DNA methylation data, with applications to aging and cancer evolution.

Depending on the research question, we use machine learning and AI to identify patterns in large datasets and integrate different data types. We implement our methods as reproducible bioinformatics tools.

Representative papers: Jeong et al., Nature Aging, 2026; Jeong et al., Nature Genetics, 2025; Jeong et al., Nucleic Acids Research, 2016; Jeong et al., Scientific Reports, 2019.

Training and mentoring

Graduate students develop skills in:

  • Reproducible analysis in Linux and high-performance computing environments
  • Large-scale genomic data analysis using Python and R
  • Long-read genome and structural variation analysis
  • Single-cell and multi-omics data integration
  • Statistical modeling and machine learning for genomics
  • Bioinformatics workflow and analysis tool development

Regular individual and lab meetings cover study design, interpretation of results, and manuscript preparation. We aim to present our work at international conferences and in research publications, and provide mentoring tailored to each student’s career goals.

Who should apply?

We welcome students who:

  • Are interested in genomics, evolution, epigenomics, aging, or disease biology
  • Want to address well-defined research questions using large 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

Applicants may come from life sciences, biotechnology, medicine, computer science, statistics, mathematics, AI, or related fields. Programming and bioinformatics experience is helpful but not required. Because all projects focus on data analysis and method development, students should be motivated to build programming and quantitative analysis skills during their graduate training.

Available positions

  • Positions3 M.S.-Ph.D. integrated or Ph.D. students
  • DepartmentDepartment of Life Sciences, GIST
  • AreasComputational genomics, bioinformatics, evolutionary genomics, epigenomics, and machine learning for genomics

How to apply

Please email the following materials:

  • CV
  • Academic transcript
  • A brief statement of research interests and motivation
  • Relevant coursework, research, programming, or data analysis experience
  • Intended enrollment term
  • Optionally, a GitHub profile or examples of previous research or analysis

After reviewing your materials, we will discuss your research interests, possible projects, and the GIST graduate admissions process in an individual meeting.

Email about graduate research
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