Research Software Engineer for Translational Biomedical AI
Pubblicato il 03-09-2026 - Human Technopole in Italia
Research Software Engineer for Translational Biomedical AI
Build the science that shapes the future of human health.
Application closing date: 15.09.2026
Join a place where ambitious science thrives
Human Technopole is a rapidly expanding life science institute in Milan, 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 Health Data Science Centre develops advanced computational and machine learning approaches to analyse complex biomedical and clinical data. The Centre works at the interface of data science, machine learning, computational biology, epidemiology, and clinical research, with the goal of transforming research models into tools that can support biomedical discovery and healthcare innovation.
We are seeking a motivated Research Software Engineer to help translate research prototypes into robust, usable, and transferable software tools. We welcome candidates at different levels of seniority.
Depending on the selected candidate’s experience, the position may be shaped as either a Junior or as a Senior Software Engineer.
This role sits at the interface between software engineering, machine learning and clinical translation. The successful candidate will work closely with data scientists, computational biologists, clinicians, ICT experts, and hospital partners to develop software systems that make advanced research models usable in real‑world biomedical and healthcare settings. This position is funded by the European Union EU4H-2026-SANTE-PJ-04 – Project: European Cardiovascular Health data and AI Network (CHAIN), GA 101314833.
Your mission
As a Research Software Engineer for Translational Biomedical AI, you will work closely with data scientists, computational biologists, clinicians, ICT experts, and external collaborators to make biomedical machine learning models easier to use,