> Markdown version of [/jobs/ext/4048183-synthetic-data-generation-for-dental-ai-master](https://www.wearedevelopers.com/jobs/ext/4048183-synthetic-data-generation-for-dental-ai-master). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Synthetic Data Generation for Dental AI (Master) - **Company:** Koninklijke Philips N.V. - **Location:** Eindhoven, Netherlands - **Experience:** Internship - **Contract:** Internship / Graduate position - **Skills:** 3d Models, 3D Scanning, 3D Computer Graphics Software, Artificial Intelligence, Computer Vision, Python (Programming Language), Machine Learning, Pytorch, Deep Learning, Generative AI, Data Generation - **Published:** October 16, 2026 - **Apply:** https://startup.jobs/internship-synthetic-data-generation-for-dental-ai-master-0050-us00-remote-us90-10497394 ## About the Role * MSc. in Data Science, CS, AI, Mathematics, Physics or Biomedical Engineering. * Experience with Python programming is essential. * Good knowledge of deep learning and computer vision in PyTorch. * Passion for 3D modelling, rendering and synthetic data generation. * Experience with Blender, 3D reconstruction or generative AI is a plus. * Language: good written and spoken English. * Previous internship experience is not necessary but is a plus. * Good communication skills and enthusiasm to work in an international team. ## Description Philips has a long history of successfully introducing innovative products to the market (lamps, CD players, medical imaging equipment, patient monitors, etc). Today, Philips is 100% health technology company with Data and AI being an integral part of Philips strategy. This internship develops a scalable pipeline for generating photorealistic synthetic intraoral images and automatic ground-truth labels from 3D jaws. Tooth detection and instance segmentation are the primary validation case. The same approach should also support other intraoral AI algorithms, such as gumline localization and detection of cavities or other oral conditions. Building on previous work in 3D jaw reconstruction and SAM mask generation/distillation, the project will quantify how synthetic data affects accuracy, robustness and the synthetic-to-real domain gap. Your challenge: We are looking for an enthusiastic candidate who is passionate about computer vision, 3D graphics and deep learning. Your team: You will be working in the R&D department of Philips Oral Healthcare in Eindhoven. The department works on cross-domain competences required to develop cutting-edge oral healthcare innovations, like machine learning, microbiology, sensors, optical imaging. Your role: * Review 3D/synthetic dental data, rendering and domain-randomization methods. * Build a data-generation pipeline from reconstructed or parametric 3D jaws. * Vary anatomy, camera pose, lighting, materials and oral conditions. * Export images with masks, depth, normals and tooth labels. * Train/evaluate tooth detection and identification models; report conclusions. * Draft a technical note or paper; present results at a Philips R&D forum.