Internship in Artificial Intelligence-Saclay- H/F

CEA; Saclay; On-site; EUR 10,000 - 13,000; Full time

Benefits offered by this job

Python and PyTorch competency

Job summary

CEA in Saclay is offering an internship in Artificial Intelligence focusing on continual category discovery within active learning. The intern will conduct literature reviews, analyze recognition of known vs new categories, select informative images for expert feedback, and compare against baselines.

Access to state-of-the-art resources and potential for publication are highlighted. The role emphasizes Python and PyTorch skills, deep learning foundations, and collaboration with the

Qualifications

• Literature review on active learning for category discovery, visual domain adaptation, and continual discovery.
• Analyze with unlabeled data to separate known vs new categories and maintain learned representations over time.
• Select informative images for expert feedback under a limited query budget and evaluate improvements against baselines.

Responsibilities

• Conduct a literature review on category discovery, visual domain adaptation and active learning.
• Analyze when changed appearances of known categories are confused with genuinely new categories.
• Select informative images for expert feedback under a limited query budget.
• Use this feedback to improve category discovery and maintain categories over time.
• Evaluate the approach against fully automatic and randomly queried baselines.

Skills

Python

PyTorch

Education

Bac+5 - Master 2

Job description

Internship in Artificial Intelligence-Saclay- H/F

Category

Engineering science

Contract

Internship

Job title

Internship in Artificial Intelligence-Saclay- H/F

Subject

Active Learning for Continual Category Discovery

As an intern at the CEA, you will have the opportunity to work within a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an ideal setting for learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work conducted may potentially lead to a scientific publication.

Context :

Most image-recognition systems are trained with labeled examples and a fixed list of categories. After deployment, however, new images continue to arrive. Their visual appearance may change because of a different style, camera, or environment, and the system may also encounter categories absent from its initial training data. In continual category discovery a model first receives labeled images from known categories, then successive collections of unlabeled images containing both known and genuinely new categories. It must recognize known categories, form groups for new ones, and retain what it has learned over time. The main challenge is that visual similarity does not always mean that two images show the same category. A known vehicle represented as a sketch may look less similar to photographs of the same vehicle than to a visually close but new vehicle category. The internship will investigate methods to resolve these ambiguities with limited expert feedback.

What do we expect from you ?

The intern will work on the following tasks :
Conduct a literature review on methods for category discovery, visual domain adaptation and active learning
Analyze when changed appearances of known categories are confused with genuinely new categories
Select informative images for expert feedback under a limited query budget
Use this feedback to improve category discovery and maintain categories over time
Evaluate the approach against fully automatic and randomly queried baselines

This internship offers an introduction to research in machine learning and computer vision. Depending on the results, it may lead to a scientific publication and may be pursued with a PhD focused on a similar topic.

#Cea-List

Methods / Means

Python, PyTorch

• Students in their final year of studies (M2 or last year of engineering school)
• Strong foundations in machine learning, deep learning and computer vision
• Interest in visual representation learning, continual discovery, or active learning
• Python proficiency in a deep learning framework

Site

Saclay

Location

Saclay

Prepared diploma

Bac+5 - Master 2

PhD opportunity

Oui

Requester

01/03/2027

Organisation

The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :

• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.

Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.

The CEA is established in ten centers spread throughout France

Reference

2026-41991

Reference

2026-41991

Within the Institute and the Department of Ambient Intelligence and Interactive Systems (DIASI), and the Artificial Intelligence, Language, and Vision Service (SIALV), the Laboratory for Semantic Analysis of Text and Image (LASTI) conducts foundational research projects in artificial intelligence applied to computer vision, automated processing, and multimodal data analysis.