PhD: Robust Few-Shot Learning for Abdominal 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

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.