FWS , Horticulture Undergraduate Research Assistant - Computational Genomics

University of Kentucky

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FWS Spring 2026, Horticulture Undergraduate Research Assistant - Computational Genomics & Bioinformatics (Plant Breeding)

• Requisition Number: DU14373
• Department Name: 1B531:
  Federal Work Study
• Work Location:
  
  Lexington, KY
• Salary: $12.00 – $13.75 per hour
• Type of Position:
  Student
• Position Time Status:
  Part‑Time
• Shift: Weekdays between 8:00 a.m. – 5:00 p.m.
• Final date to receive applications: 05/15/2026

Must be a currently enrolled undergraduate student in a related field (e.g., Bioinformatics, Computer Science, Data Science, Statistics, Biology, Plant & Soil Sciences, Horticulture, Biotechnology).

• Demonstrated interest in computational work (coursework, projects, or prior experience) and willingness to learn bioinformatics.
• Comfort working with computers and learning new tools quickly.

Minimal physical requirements; ability to work at a computer for extended periods. Occasional in‑person meetings and onboarding on campus.

• Support computational analysis of genomic and transcriptomic datasets for plant breeding and genetics projects.
• Run and troubleshoot workflows on UKY HPC resources (LCC/MCC) (job scheduling, file transfers, storage organization, best practices).
• Develop and maintain reproducible analysis pipelines (e.g., Bash, Python, R, workflow tools; version control with Git).
• Perform or assist with analyses such as GWAS (e.g., GAPIT or R‑based workflows), population structure (PCA), kinship, and visualization, QTL mapping / QTL‑seq / QTL‑seq‑like pipelines (variant filtering, allele frequency analysis, peak detection), RNA‑seq analysis (QC, alignment/quantification, differential expression, summary plots).
• Help with data QC/organization (metadata, sample sheets), documentation, and creation of "how-to" notes for lab workflows.
• Communicate progress in weekly check‑ins and maintain clear records of commands, parameters, and outputs.

Strong attention to detail, organized file/data management habits, and ability to document work clearly. Curiosity about genetics/genomics, interest in applying computation to plant science problems, and motivation to build practical skills. Ability to work independently while also communicating clearly in a collaborative research team.

• Experience with Linux/command line, basic scripting, or working on a shared computing system.
• Familiarity with R and/or Python for data analysis and visualization.
• Familiarity with Git/Git Hub or reproducible research practices.
• Exposure to any of the following is a plus (not required): GWAS/QTL concepts, RNA‑seq, variant calling, workflow managers (Snakemake/Nextflow), HPC schedulers (e.g., SLURM).