Master's internship Data Science & Machine Learning - Computer Vision in Vehicle Recognition

You have knowledge of and perhaps experience with:

Because all communication during the internship - both oral and written - takes place in Dutch, a good command of the Dutch language is necessary.

A pleasant learning environment in which you:

In return for your commitment, you will receive an internship allowance of € 800,- gross per month (based on 40 hours per week).

You will develop a self-learning model that analyzes public livestreams from traffic cameras and counts passing vehicles. Based on the length of the vehicle, you will distinguish between freight vehicles and other vehicles. By the end of the internship, you will have a working prototype with which you can demonstrate whether these counts are a valuable addition to or improvement upon current sources.

Together with your internship supervisor, you will coordinate how you can work hybridly, with flexible working hours and the option to work partly from home on your internship assignment.

Are you that master's student who wants to bridge the gap between the application of artificial intelligence and classical statistical methods in a real, operational environment?

Do you want to use machine learning to map traffic flows more intelligently?

You are pursuing a master's degree with a specialization in Data Science & Machine Learning.

• R/Python

• Computer vision analytics (image classification, object detection)

• quantitative research

Keywords that describe you are enthusiastic, independent, flexible, shows initiative, proactive, tactful, team player, social, communicative.

• work together with experienced colleagues from various backgrounds;

• learn to set up research independently.

• become proficient in rigorously comparing and validating two data sources, so that you can substantiate whether the new source can serve as a supplementary dataset;

• increase your communication skills and can clearly formulate and present your research results;

• experience what is required to design, train, and make operational a self-learning machine learning model (vehicle recognition and counting).