Remote-friendly Research Engineer, Multimodal Brain Data

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

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.