CT成像基础模型鲁棒少样本学习博士研究员

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

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Contract type : Fixed-term contract

Level of qualifications required : Graduate degree or equivalent

Fonction : PhD Position

Level of experience : Recently graduated

About the research centre or Inria department

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .

The centre has 41 project teams , 27 of which operate jointly with Paris-Saclay University (15 teams) and the Institut Polytechnique de Paris (12 teams). Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.

The centre also hosts the Institut DATAIA , dedicated to data sciences and their disciplinary and application interfaces.

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.

In this context, we are seeking for a PhD candidate with an excellent background in AI and mathematics, to design robust few-shot learning methods to allow the on-site adaptation of the foundation model and generalization to specific diagnostic tasks, such as prediction and segmentation of CT images of all body regions, without requiring massive re-annotation efforts nor GPU resources. The work will build upon the expertise of the OPIS team on few-short learning [2,3,4,5].

[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

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.

Benefits package

• 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

Remuneration

2300€ gross/month

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General Information

• Theme/Domain : Optimization, machine learning and statistical methods

Statistics (Big data) (BAP E)

• Town/city : Gif sur Yvette

• Inria Center :

Centre Inria de Saclay

• Starting date : 2026-12-01

• Duration of contract : 3 years

• Deadline to apply : 2026-10-31

Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.

Instruction to apply

Defence Security :

This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.

Recruitment Policy :

As part of its diversity policy, all Inria positions are accessible to people with disabilities.

Contacts

• Inria Team :

OPIS

• PhD Supervisor :

Chouzenoux Emilie / emilie.chouzenoux@inria.fr

The keys to success

We seek for a talented candidate with Master 2 / Engineering degree, with a solid background in optimization, statistics, and a strong motivation for the medical imaging field. Experience in Python programming is necessary. An experience in PyTorch or TensorFlow is highly recommended.

The candidates are requested to send a CV and a motivation letter to apply for this position.

About Inria

Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.