Exciting opportunity for someone who can turn massive genetic datasets into real therapeutic insights. If you want to own the work end-to-end and see it matter, keep reading.

Role

• Process and analyse large-scale multi-omics and population-scale genetic data (GWAS summary stats, molecular QTLs, WGS/WES).
• Run post-GWAS integration — colocalization, Mendelian Randomization, transcriptomic and proteomic data — to identify high-confidence drug targets.
• Build and apply ML/deep learning models tailored for functional genomics.
• Design and maintain production-grade, automated analysis pipelines.

Requirements

• PhD or MSc in Statistical Genetics, Computational Biology or Bioinformatics.
• Hands‑on experience with genetic association studies and multi‑omics integration.
• Strong coding skills and a commitment to clean, automated pipelines.
• Experience with deep learning architectures...