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

Inria; Paris; Hybrid; EUR 45,000 - 60,000; Full time

Benefits offered by this job

Subsidized meals

Partial public transport reimbursement

Leave: 7 weeks + RTT days

Teleworking option

Professional equipment available

Social, cultural and sports events

Job summary

Inria ARAMIS team at the Paris Brain Institute (ICM) on the Pitié-Salpêtrière campus seeks a software-focused researcher to build multimodal representation pipelines. You will work under Reuben Dorent to extract and store representations from histology, spatial transcriptomics and MRI, and contribute to evaluation protocols and reproducible workflows.

The role emphasizes Python and PyTorch, deep learning, and collaboration with a PhD student.

Qualifications

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

Responsibilities

• Write data loaders and preprocessing for public datasets such as HEST-1k, TCGA, Ivy GAP and the Allen Human Brain Atlas.
• Extract embeddings from several pretrained foundation models per modality and organize them in a common format.
• Implement the evaluation protocol with train/test splits and retrieval/matching metrics.
• Extend the alignment method to three modalities and analyze results.
• Package code with tests, docs and notebooks for open-source release.

Skills

Python

PyTorch

Deep learning

Self-supervised learning

Git & code review

Tools

SLURM

Job description

Contexte et atouts du poste

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.

Mission confiée

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.

Principales activités

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

Compétences

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

Other valued appreciated :

Avantages

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage