Data Science Internship: Learning tooth descriptors

6-9 months assignment, 40h/a week, allowance 500-700 Euro

Philips is a 100% health technology company with Data and AI being an integral part of its strategy. This assignment focuses on learning robust representations through self-supervised learning applied to large-scale intraoral teeth images. By developing meaningful tooth embeddings, we aim to enhance the performance of downstream tasks such as tooth classification, segmentation, and detection. Additionally, these learned representations could serve as unique identifiers for individual teeth, enabling reliable re-identification across extensive dental image databases.

Your role:

• Conduct a review of self-supervised/contrastive methods and techniques.
• Implement various self-supervised/contrastive methods.
• Develop robust representations (signatures) for individual teeth.
• Train variants of our AI models leveraging the self-supervised weights.
• Benchmark AI models to quantify improvement due to self-supervised learning.
• Document the results in a technical report.

You're the right fit if:

• Currently studying towards your Master WO (MSc.) in Data Science, Mathematics, Applied Mathematics, Computer Science, Physics, Electrical/Electronic/Biomedical engineering.
• Experience with Python programming is essential.
• Good knowledge of machine learning practices in PyTorch.
• Experience with state-of-the-art AI models in computer vision.
• Good written and spoken English.
• Previous internship experience is not necessary but a plus.
• Good communication skills and enthusiasm to work in an international team.

How we work together
We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.

About Philips
We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.