Postdoc in Digital Pathology

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 (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  , 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 skills
You will enhance your scientific and professional skills by working with:

• 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-supervised learning, foundation models, etc.
• Be part of a wider AI community, specifically the multimodal AI flagship project taking place at HT

Human Technopole also supports career development through training, mentoring and dedicated learning opportunities.

What you’ll bring

Essential

• A PhD in machine learning / computer science or a related discipline.
• Strong programming and data handling skills (e.g. git, HPC environments, etc)
• Fluency in English

Preferred 

• Machine learning experience applied to spatial transcriptomics and/or digital pathology datasets
• Experience in image registration, multimodal modelling (images, text)
• Prior impactful publications in a relevant field

Organisational and social skills

• Self-motivated, able to work independently and organize own workload
• Enthusiastic team player and able to work collaboratively and across different disciplines.
• Strong problem-solving abilities.
• Networking and public speaking proficiency.
• Ability to evaluate research findings, troubleshoot technical issues, and draw meaningful conclusions.

Why Human Technopole
HT offers an international and dynamic workplace, competitive welfare provisions, flexible working policies and relocation support. Researchers moving to Italy may benefit from attractive tax benefits. We promote work–life balance and provide parental support initiatives.

How to apply
Submit:

• CV
• Motivation letter (English)
• Contact details of two referees

This is a 4-year contract offered under CCNL Chimico Farmaceutico, Level B2

Salary: up to € 43.000,00 depending on the candidate’ seniority + €2.000 welfare
The position is based in Milan, Italy, within our vibrant international campus.

We strongly encourage applications from candidates belonging to protected categories (L. 68/99).

The Foundation reserves the right, at its sole discretion, to extend, suspend, modify, revoke, or cancel this job posting without giving rise to any rights or claims whatsoever in favor of the candidates; the Foundation reserves, however, the right not to proceed with the awarding of the above-described assignment due to the effect of supervening regulatory provisions and/or obstructive circumstances.

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