Post-doctorat en pathologie numérique; F​/H

Postdoctoral Position

The position will be shared between the team of Pierre Bost and the team of Manuel Rodrigues. The Bost team has recognized expertise in the analysis of spatial biological data, particularly in the development of new computational methods to analyze spatial transcriptomics and proteomics data. The Rodrigues team specializes in translational oncology, with a particular focus on tumor genomics and DNA repair mechanisms in uveal melanoma.

They develop advanced experimental models to study the early stages of tumorigenesis, the dynamics of the tumor microenvironment, and therapeutic resistance strategies.

The postdoc will interact with both teams and benefit from joint supervision by the principal investigators of the two teams.

Research Project

The objective of this project is to develop new computational methods to analyze the spatial architecture of tumors and determine if the spatial tumor organization can constitute a predictive biomarker of patient survival and response to treatments.

As a proof of concept, the postdoc will work on liver metastases of uveal melanoma, a rare and still poorly understood disease characterized by a poor prognosis and limited therapeutic options. A large collection of histology, spatial transcriptomics, and single-cell RNA-seq data has been generated by the Rodrigues team.

The candidate will combine histopathological and spatial transcriptomics data using fundamental models applied to histology to develop a neural network capable of predicting cell types from histological images alone.

This network will then be applied to a large clinical cohort, allowing for the spatial distribution of each cell type in the samples to be obtained. Computational tools developed within the Bost team will then be used to extract spatial features from these distributions and exploit them to predict patient survival and genetic profile.

References

[1]

F. Mangane et al., "Exploiting pair correlation function to describe biological tissue structure," [Dec. 22, 2025]

[2] P. Bost et al., "Statistical modeling and analysis of cell counts from multiplexed imaging data," Cell Systems, vol. 16, no. 6, p. 101296, [Jun. 2025]

[3]

F. Hörst et al., "CellViT:
Vision Transformers for precise cell segmentation and classification," Medical Image Analysis, vol. 94, p. 103143, [May 2024]

[4]Le Ven A et al, Prevalence of the Predisposing Gene MBD4 for Uveal Melanoma. JAMA Ophthalmol. 2026 [Apr 30 2026]

[5] Rodrigues et al, Prospective assessment of circulating tumor DNA in patients with metastatic uveal melanoma treated with tebentafusp, Nat Commun. [2024 Oct 14]

[6] Silveira AB et al, Base-excision repair pathway shapes 5-methylcytosine deamination signatures in pan-cancer genomes, Nat Commun. [2024 Nov 14]

Education and Skills

Main Requirements

• Doctorate (PhD) in image analysis, digital pathology, or data analysis.
• Practical expertise in deep learning (Tensor Flow or PyTorch).
• Basic knowledge of biology and oncology.
• Proficiency in English.

Additional Assets

• Expertise in histology and anatomic pathology.
• Knowledge of spatial statistics, particularly point pattern analysis.

Required Skills

Ability to work in a highly interdisciplinary environment combining histology, oncology, and spatial statistics.

Ability to learn new subjects and fields quickly.

All our opportunities are open to people with disabilities.

Contract Information

Type of Contract : COD

Start Date: September 2026

Duration of Contract : 36 months

Work Time :
Full-time- daily allowance

Remuneration : according to the current scales

Benefits :
Collective catering, 70% annual transport pass coverage, company health insurance

Location of the Position :
Paris

Contact

To apply, please send CV, cover letter, and 2 references.

Publication date of the offer: 01/06/2026

Final date to receive applications:
As soon as filled

The Institut Curie is an inclusive employer respecting equal opportunities.

It also commits to applying stringent standards in research integrity.