2026 Computational Data Scientist Intern (PhD)

Role Summary

2026 Computational Data Scientist Intern (PhD). This internship focuses on comparing and leveraging variant annotation tools and databases to annotate variants from real sequencing datasets, with the goal of prioritizing those most likely to have functional impact in association studies.

Responsibilities

• Survey and compare major variant annotation tools (e.g., VEP, SnpEff, ANNOVAR).
• Compare the gene and transcript lists of humans in RefSeq and Ensembl/GENCODE.
• Explore and integrate multi-source annotations (e.g., ClinVar, HGMD, splice-disrupting variants in SpliceAI, functional scores in FAVOR, AlphaMissense & AlphaGenome, and regulatory features).
• Annotate the variants in a WGS project using selected tools and support/conduct association analysis.

Qualifications

• Required: Currently enrolled in university, pursuing a PhD in Genome sciences, Bioinformatics, Biostatistics, Human genetics, Computer Science or other related fields.
• Required: Must be enrolled in university for at least one semester following the internship.
• Required: Expected graduation date between December 2026 ‚Äì July 2027.
• Required: Proficient in programming languages (R or Python).
• Required: Strong written and verbal communication skills.
• Required: Self-directed and able to work with minimal supervision.
• Preferred: Have experience performing variant annotation and prioritizing variants based on their annotations.
• Preferred: Strong quantitative and analytical skills.
• Preferred: Energetic and eager to tackle new projects and generate new ideas.

Skills

• Genomics data analysis
• Variant annotation tools (VEP, SnpEff, ANNOVAR)
• Bioinformatics programming (R, Python)
• Data integration from multiple annotation sources
• Association analysis and interpretation

Education

• PhD in a relevant field (as listed in Qualifications)

Additional Requirements

• None applicable beyond travel/physical requirements not specified as essential