Machine Learning Engineer


Currently, Praxa Sense is looking for a computer science student who is interested in an opportunity to improve and further develop a machine learning algorithm to detect the most common heart rhythm disease called Atrial Fibrillation (AF). The existing model is based on Convolutional Neural Network (CNN) technique. Public dataset of arrhythmia patients has been used to train and test the model. Your challenge will be to optimize the data preparation such as noise detection and correction and ECG-lead conversion to improve the model in detecting AF.


AF is the most common cardiac arrhythmia, affecting around 3% of adults and 10% of those aged above 75 years. Heart rhythm diseases are hard to recognize due to the nature of the symptoms. Current tools used in the medical field are not suited in early diagnosis because of the short measuring times and inefficient algorithms. We will bridge this gap by enabling early diagnosis with a long-term user-friendly device. By detecting heart rhythm diseases early, serious consequences, like strokes, can be prevented.


We are looking for a bachelor/master student in computer science or related fields, a team player who is strong and proactive in communication and wants to learn and exercise his/her knowledge in practice. The student should have a basic understanding of programming in Python and machine learning models like (convolutional) neural networks.

Praxa Sense is a medical tech start-up company located in the inspiring environment of YES!Delft and currently working on developing Afi: An unobtrusive long-term monitoring device that will enable the early detection of AF.

- A young and professional and yet down-to-earth team with different expertises

- Lots of freedom to implement your ideas

- Opportunity to be hired after study

- Internship compensation €300,- per month

- Free lunch every working day and free beers on Friday!

Interested and want to know more? Contact