Context Image segmentation is a fundamental task in computer vision, with growing importance in healthcare applications such as automated monitoring, diagnosis support, and quantitative analysis. While deep learning has led to major advances in segmentation accuracy, most existing methods operate at a single level of detail, neglecting the hierarchical structure present in many medical and biological scenes where regions can be organized into coarse and fine levels (e.g., global areas and subregions). Hierarchical image segmentation addresses this challenge by modeling semantic relationships between global and fine-grained regions. By combining multi-scale representations and structured reasoning, these models can achieve more interpretable, consistent, and clinically meaningful predictions. This internship aims to develop and evaluate deep learning architectures for hierarchical segmentation of RGB healthcare images. The goal is to improve both the accuracy and the interpretability of segmentation models in real-world clinical scenarios. Missions The intern will contribute to the design, implementation, and evaluation of hierarchical deep learning models for healthcare-related image segmentation. The main objectives and tasks include: Conduct a literature review on deep learning approaches for hierarchical segmentation in both computer vision and medical imaging. Design and implement hierarchical segmentation architectures. Experiment with training strategies, including knowledge distillation, self-supervised learning, and domain adaptation to handle variability in imaging conditions. Evaluate model performance on publicly available datasets and benchmark results against standard single-level segmentation baselines. Analyze hierarchical consistency and interpretability Required Qualifications The candidate should meet the following criteria: Education: Currently enrolled in an engineering school or pursuing a Master’s degree (or equivalent); Technical skills: Strong knowledge of computer vision and deep learning; Programming: Excellent command of Python and PyTorch; Ability to work independently with rigor, initiative, and strong organizational skills; Familiarity with software development best practices is highly appreciated. Duration: 6 months Location: Multitel, Parc Initialis 2, Rue Pierre et Marie Curie, 7000 Mons, Belgium. Application process: Interested candidates should email their application (single PDF named Lastname_Firstname_InternshipTitle.pdf, including CV, cover letter, and academic transcript), indicating their intended start and end dates and the internship title in the email subject line. Depending on the candidate’s profile and interests, the internship may take either a research-oriented or an industry-oriented focus. Multitel is a research and technological innovation center based in Mons, Belgium, supporting industrial players in the development of cutting-edge technological solutions in Artificial Intelligence, Applied Photonics, Networks and Cybersecurity, IoT and Embedded Systems, and Railway Certification. With its multidisciplinary expertise and commitment to excellence, Multitel actively contributes to strengthening industrial competitiveness and fostering innovation at both regional and international levels. Multitel’s Artificial Intelligence Department has strong expertise in machine learning and data-driven technologies, applied to a wide range of data modalities (images, videos, time series, 3D point clouds, spectral and satellite data, and audio signals) as well as diverse application domains (healthcare, agriculture, defense, security, etc.). The department has expertise covering the entire data value chain, from data collection and preprocessing to model development, deployment, and real-world integration