Dental AI Synthetic Data Intern: 3D Rendering & Labels

Philips; Eindhoven; Hybride; EUR 5 600 - 7 800; Plein temps

Avantages offerts par ce poste

Housing compensation

Travel compensation

Paid holidays

Résumé du poste

Philips invites MSc students to join an internship in Eindhoven focused on synthetic data generation for dental AI. You will build a scalable pipeline to create photorealistic intraoral images and ground-truth labels from 3D jaws, enabling tooth detection and segmentation validation.

You will explore domain randomisation, rendering, and model training in PyTorch, with hands-on experience in 3D modelling, Blender and generative AI.

Qualifications

• 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.

Responsabilités

• Review 3D/synthetic dental data, rendering and domain-randomisation 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.

Connaissances

Python programming

PyTorch

3D modelling

English

Generative AI

3D reconstruction

Blender

Formation

MSc in Data Science/CS/AI/Math/Physics/Biomedical Engineering

Outils

Blender

3D reconstruction

Description du poste

Philips invites MSc students to join an internship in Eindhoven focused on synthetic data generation for dental AI. You will build a scalable pipeline to create photorealistic intraoral images and ground-truth labels from 3D jaws, enabling tooth detection and segmentation validation.

You will explore domain randomisation, rendering, and model training in PyTorch, with hands-on experience in 3D modelling, Blender and generative AI.