Research Software Engineer
Pubblicato il 02-10-2026 - Human Technopole in Milano
Build the science that shapes the future of human health.
Application closing date: 10.10.2026
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 Glastonbury Group uses machine learning applied to large-scale digital pathology and spatial transcriptomics datasets generated in-house to understand the molecular basis of specific pathologies and the genetic basis of multiple, complex diseases.
We are seeking an ambitious Software engineer with expertise in machine learning, reproducible code, computational imaging and bioinformatics/computational biology workflows related to genomics. The successful candidate will work at the interface of machine learning, pathology and genomics to help enable and accelerate the work of team members in the group (postdocs, PhD students).
You will work with the principal investigator and team to develop and productionise commonly used computational workflows and to harmonise tool/software usage across the lab. You will maintain code (GitHub/Gitlab) used across the lab, be responsible for spotting usage patterns in different projects, harmonising them into a common software stack and offering solutions that can be used in different contexts. The candidate will have the ability to work on several different digital pathology and spatial transcriptomics projects taking place in the group.
Grow Your Skills
You will enhance your scientific and professional skills by working with a range of different data modalities and across a range of scientific projects:
- Large-scale whole slide imaging (WSI) histology datasets (H&E;, IHC)
- Spatial transcriptomics
- Human genetics data (WGS)
- Text based pathology reports
- Cutting edge ML approaches in self-superv