PhD Position F/M PhD - Robust few-shot learning for foundational model in CT imaging

1000scholars; Gif-sur-Yvette; On-site; EUR 19,000 - 23,000; Full time

Benefits offered by this job

Informatic material expenses covered

Job summary

Inria Saclay, joining the OPIS team on the CentraleSupélec Saclay campus, invites a PhD student to develop few-shot learning techniques for CT image classification and tumor segmentation, with robustness analysis and validation on public and AP-HP datasets.

The project is supervised by Emilie Chouzenoux (Head of OPIS) and offers an international, creative research environment with regular seminars and access to computational resources; starting 1 Oct 2026.

Qualifications

• Fluent in English and/or French.
• Strong knowledge of Python and PyTorch for model development.
• Experience with deep learning and scientific computing is valued.

Responsibilities

• Develop few-shot learning methods for CT image classification.
• Develop models for few-shot tumor segmentation in Python/PyTorch.
• Analyze robustness and generalization capabilities of the models.
• Validate on public datasets and EDS-APHP datasets.

Skills

Python/PyTorch

Deep learning

Mathematical optimization

Convergence analysis

English & French fluency

Education

Master's degree in CS/Math/AI

Tools

PyTorch

Python

Git

Job description

Context

The Greater Paris University Hospitals Data Warehouse (EDS AP-HP) contains multimodal clinical data (PMSI, imaging, biological, and clinical documents) for over 14 million patients. The ANR FM2AI projet proposes to leverage 50,000 real-world clinical 3D CT scans from this exceptional data resource, to deploy a novel foundation model for abdominal-pelvic CT Imaging. The approach is designed to generalize across multiple clinical applications involving abdominal CT images, by resorting to self-supervised learning techniques for training the foundation model, and then exploiting it for a wide class of clinical queries thanks to the innovative few-shot learning paradigm [1], while paying attention to robustness assessment.

[1] E. Pachetti, S. Colantonio, A systematic review of few-shot learning in medical imaging, Art. Int. Med., 2024.

[2] S. Martin, M. Boudiaf, E. Chouzenoux, J.-C. Pesquet, et al., Towards practical few-shot query sets: Transductive minimum description length inference, Proc. the Int. Conf. on Neu. Inf. Proc. Sys. (NeurIPS), 2022.

[3] S. Martin, Y. Huang, F. Shakeri, J.-C. Pesquet, I. Ben Ayed, Transductive zero-shot and few-shot CLIP, IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024.

[4] L. Zhou, F. Shakeri, A. Sadraoui, M. Kaaniche, J.-C. Pesquet, I. Ben Ayed, UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning, IEEE/CVF Conf. on Comp. Vision and Patt. Recognition (CVPR), 2025.

[5] M. Vu, E. Chouzenoux, J.-C. Pesquet, I. Ben-Ayed. Aggregated f-average Neural Network applied to Few- Shot Class Incremental Learning, vol. 237, pp. 110054, Signal Processing, 2025.

Assignment

Missions

• Develop new few-shot learning techniques for CT image classification
• Develop new model for few-shot tumor segmentation
• Analyze robustness and generalization capabilities of the models
• Validation on public datasets and EDS-APHP datasets.

Environment

The phd student will be supervised by Emilie Chouzenoux (Head of OPIS team, Inria Saclay), and will interact regularly with the members of the ANR FM2AI consortium. The student will join the Inria Saclay team OPIS (https://opis-inria.eu/). He/she will be located in the Centre de la Vision Numérique, in CentraleSupélec campus, Saclay, France. He/she will enjoy an international and creative environment where research seminars and reading groups take place very often. Informatic material expenses will be covered within the limits of the scale in force.

Starting date is flexible, from the 1st Oct. 2026.

Main activities

• Programming in Python/PyTorch environment
• Bibliographical study
• Deep learning architecture design/training/testing
• Mathematical optimization / convergence analysis
• Writing of scientific reports

Skills

Languages: The candidate must be fluent in english and/or french languages.