Research Engineer F/M – Aligning medical foundation models across histology, spatial transcriptomics and MRI

Inria Paris; Paris; On-site; EUR 42,000 - 54,000; Full time

Job summary

Inria Paris within ARAMIS at the Paris Brain Institute (ICM) invites applications for a research engineer. The role focuses on building reproducible pipelines that extract representations from foundation models across histology, spatial transcriptomics, and MRI, and organizing them for analysis.

You will work with a PhD student to extend two-modality alignment to three modalities, develop data loaders, evaluations, and reusable tools, and contribute to reports and publications.

Qualifications

• Strong Python programming skills with practical experience in PyTorch and deep learning.
• Knowledge of representation learning or self-supervised learning.
• Good software practices: Git, code reviews, testing, documentation, and using GPU clusters (SLURM).

Responsibilities

• Write data loaders and preprocessing for public datasets (e.g., HEST-1k, TCGA, Ivy GAP, Allen Brain Atlas).
• Extract embeddings from pretrained foundation models per modality and organize them in a common format.
• Implement evaluation protocols with train/test splits and robust metrics for few-pair settings.
• Extend alignment method to three modalities; analyze results against baselines.
• Package code with tests, docs, and notebooks for open-source release.
• Participate in team meetings and contribute to progress reports and papers.

Skills

Python programming

PyTorch

Deep learning

Representation learning

Self-supervised learning

Git

Code testing

Documentation

GPU clusters

Tools

PyTorch

Git

SLURM

Job description

Context

The position is in the ARAMIS project-team (Inria, CNRS, Inserm, Sorbonne Université), at the Paris Brain Institute (ICM) on the Pitié-Salpêtrière hospital campus. ARAMIS develops computational and statistical methods for multimodal patient data in brain disorders. The engineer will be supervised by Reuben Dorent (Inria research scientist), whose work focuses on multimodal representation learning and statistical inference from incomplete medical data.

Assignment

Assignments : With the help of Reuben Dorent, the recruited person will be taken to:

• Build a reproducible pipeline that extracts and stores representations from several foundation models per modality (histology, spatial transcriptomics, MRI) on public multimodal datasets
• Set up an evaluation protocol for alignment from few pairs, using datasets in which the true correspondences are known but only a few hundred of them are revealed during training
• Extend the team's two-modality alignment method [1] to a first setting with three modalities, and compare it with existing baselines
• Support a PhD student of the team working on histology-spatial transcriptomics integration, by providing shared tools for data preprocessing, feature extraction and evaluation

For a better knowledge of the proposed research subject :

[1] J. Kordes, R. Dorent. Representation Alignment from a Few Paired Data via Distilled Optimal Transport. arXiv:2610.12781, 2026. [2] S. Ruyter, R. Dorent et al. Program-space Diffusion for Morphology-to-Transcriptomics Prediction. COMPAYL workshop, MICCAI 2026. [3] J. Ostermaier, S. Ruyter, R. Dorent et al. Disentangled shared representations improve morpho-transcriptomic integration. COMPAYL workshop, MICCAI 2026.

Collaboration

The recruited person will be working closely with a PhD student of the ARAMIS team.

Responsibilities

• Write data loaders and preprocessing for public datasets such as HEST-1k (paired histology and spatial transcriptomics), TCGA (MRI, histology and clinical data), Ivy GAP and the Allen Human Brain Atlas
• Extract embeddings from several pretrained foundation models per modality (e.g. pathology, single-cell / spatial transcriptomics and 3D MRI models) and organise them in a common, documented format
• Implement the evaluation protocol: train/test splits that reveal only a few hundred pairs, retrieval and matching metrics, and robustness to the choice of paired samples
• Implement and run baselines and the extension of the team's alignment method to three modalities; analyse the results
• Package the code (tests, documentation, example notebooks) so that it can be reused by the PhD students of the team and released as open-source software
• Take part in team meetings, present results and contribute to the writing of technical reports and, where results allow, scientific publications

Main activities

• Analyse and implement related work
• Design novel innovative solutions
• Write progress reports and papers
• Communicate advancement to the team

Skills

Technical skills and level required :

• Solid Python programming and experience with PyTorch
• Practical experience in deep learning; knowledge of representation learning or self-supervised learning
• Good software practices: git, code review, testing, documentation, use of GPU clusters (SLURM)

Languages :English

Relational skills : Good communication skills