Statistical Geneticist
Pubblicato il 23-08-2026 - Human Technopole in Italia
Build the science that shapes the future of human health.
Application closing date:
Join a place where ambitious science thrives
Human Technopole (HT) is an international research institute dedicated to advancing human health through interdisciplinary life science research. Located in Milan's MIND innovation district, HT brings together scientists from around the world to tackle fundamental questions in biology using state‑of‑the‑art technologies and large‑scale datasets.
Within this mission, the Biostatistics Unit of the Centre for Genomics – Population and Medical Genomics Programme provides statistical expertise across research projects, supporting the analysis of complex human genetic and multi‑omics data to uncover the biological mechanisms underlying health and disease.
Your mission
As a Statistical Geneticist, you will provide statistical and computational expertise to support cutting‑edge genomic research across the Centre.
You will:
- Lead and conduct advanced statistical genetics analyses, including GWAS, polygenic risk scores, fine‑mapping,
heritability estimation, genetic correlation, Mendelian randomisation, multi‑omics integration, and related computational analyses using large‑scale genetic, genomic, multi‑omic, phenotypic, clinical, imaging, and electronic health record datasets.
- Advise research group leaders, collaborators, PhD students, postdoctoral researchers, and junior analysts on appropriate statistical, genetic, epidemiological, computational, machine‑learning, and AI‑based approaches to address biological and clinical research questions.
- Translate complex research questions into robust and reproducible analytical strategies, including study design, data requirements, model selection, pipeline development, sensitivity analyses, documentation, interpretation of results, and transparent reporting.
- Develop, apply, evaluate, and validate methods for deriving embeddings, latent variables, scores, and other quantitative representations of mul