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# InternPerceptual Color Processing And Gamut Mapping With Deep Learning Techniques. H/F

- **Company:** The Assistant
- **Location:** Cesson-Sévigné, France
- **Salary:** €12,000 - €18,000
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Neural Networks, C++ (Programming Language), Image Quality, Python (Programming Language), Tensorflow, Pytorch, Deep Learning, Convolutional Neural Networks, Information Technology, Data Pipelines
- **Published:** October 27, 2026
- **Apply:** https://www.stage.fr/job/21585341/apply/

## About the Role

Currently pursuing a master's degree in computer science, electrical engineering, applied mathematics, or a related field. Master 2 level, 6 months internship.

Skills in Python and C/C++.

Experience with a deep-learning framework such as PyTorch or TensorFlow, including the training and evaluation of convolutional neural networks.

Knowledge of image processing, display technologies such as OLED or local-dimming LCD, or perceptual image quality is a plus.

Fluent English An autonomous, curious, and rigorous approach to research and experimentation.

## Description

The Video Production Services team develops technologies that enhance video production and delivery for modern displays, with a particular focus on high dynamic range (HDR) and wide color gamut. This internship will investigate perceptually aware color-processing techniques that reflect key characteristics of human vision, including the effects of luminance on color perception and chromatic contrast.

As part of the team, the intern will assess the state of the art in perceptually aware color gamut mapping, identify promising approaches, design and implement prototypes, and evaluate the resulting methods. Deep learning and neural networks techniques will be preferred where they offer a meaningful advantage.

Key Responsibilities

Review and synthesize current research on perceptually aware color gamut mapping.

Identify promising directions for integrating human-vision metrics into color-processing pipelines.

Design and implement optimization methods, including neural-network-based approaches

Evaluate experimental results and iteratively refine the proposed methods.