Internship - learning imu representation

6-9 months assignment, 40 hours a week

Philips is a 100% health technology company with Data and AI being an integral part of its strategy. This assignment focuses on learning representations using self-supervised learning on large amounts of unlabeled IMU data. The IMU data is collected from brushing sessions with high-end Philips Sonicare toothbrushes and is used as input to AI models to predict where in the mouth a user is brushing. By improving the internal representations, the goal is to enhance the accuracy of these AI models. Additionally, the learned representations can be used to better characterize toothbrush motion and detect outliers.

Your role:

• Conduct a review of self-supervised methods and techniques.
• Implement various self-supervised methods.
• Train variants of our localization model leveraging the self-supervised weights.
• Benchmark localization models to quantify improvement due to self-supervised learning.
• Explore opportunities for better characterization and outlier detection.
• Document the results in a technical report.

You're the right fit if:

• Currently enrolled in a Master’s program (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.
• Interest in healthcare technology and signal processing.
• Experience with state-of-the-art AI models in computer vision / signal processing.
• Language: 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.