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Postdoc Glastonbury Lab

Pubblicato il 26-09-2026 - Human Technopole in Milano

Build the science that shapes the future of human health.

Pronto/a a candidarsi? Prima di farlo, si assicuri di leggere tutti i dettagli relativi a questo lavoro nella descrizione sottostante.
Application closing date: 15.09.2026
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 Postdoc with expertise in machine learning, computational imaging and/or spatial transcriptomics to develop novel approaches for the quantitative analysis of neuropathology across a unique collection of approximately 30,000 whole-slide images (H&E;, IHC)



from individuals with Alzheimer’s disease, related dementias and controls.
The successful candidate will join the Scientific AI Flagship Research Programme, working at the intersection of machine learning, pathology, and genomics to study brain pathology through multimodal data integration, including tissue imaging, spatial transcriptomics, neuropathology reports, and genomic data.
This project offers a unique opportunity to build computational frameworks that connect tissue morphology, molecular state, clinical pathology and genetic risk across neurodegenerative diseases. The candidate will have the ability to work on several different digital pathology and spatial transcriptomics projects taking place in the group.
Grow Your SkillsYou will enhance your scientific and professional skills by working with:
- Large-scale whole slide imaging (WSI) histology datasets (H&E;, IHC)
- Spatial tra

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