Research Software Engineer Full-time
Pubblicato il 12-09-2026 - Human Technopole in Milano
Build the science that shapes the future of human health.
Join a place where ambitious science thrives
Human Technopole (Milan) is a rapidly expanding life science institute where international researchers and cutting-edge technologies converge to accelerate biomedical discovery. Our mission is to transform bold scientific ideas into advances that improve human health.
Within this mission, the Ganna Group in the Health Data Science Centre, develops next-generation AI methods to integrate population-scale healthcare data with human genetics. The team comprises data scientists, biologists, and medical doctors, and is based across two locations: the Institute for Molecular Medicine Finland ( University of Helsinki) and Human Technopole. The lab has a strong track record of high-impact publications. Our research increasingly relies on large and complex computational infrastructures.
We are seeking a motivated Research Software Engineer who enjoys working at the interface between software engineering, artificial intelligence and biomedical research.
You will support the activities of the Ganna Group in the Health Data Science Centre and you will work closely with researchers developing AI models using some of the world’s largest longitudinal health datasets, including nationwide data from Finland and France, as well as regional data from Lombardy. These resources comprise billions of healthcare events and can be linked with genetic and multi-omics data. You will also vantaggi from collaborations with leading international institutions, including the University of Helsinki, ELLIS Institute Finland, EMBL-EBI, the Broad Institute of MIT and Harvard, and other global partners, providing valuable opportunities to engage with prominent research groups in machine learning, human genetics, and computational biology.
As a Research Software Engineer , you will be embedded directly within the research team, working closely with scientists developing innovative machine-learning