[M] Machine Learning

MASTER Assignment

internships, research and graduation projects in Machine Learning

Type: External/Internal Master CS 

Below you find a list of possible ML projects within or for companies, could be an internship, graduation project or just a Capita Selecta. I will not give detailed descriptions of the possible projects because these would be outdated within a month.

If you are interested please contact :

External Master

Philips Medical Systems

Deep learning models for blood pressure estimation using a smart watch.

TNO The Hague

Machine Learning in the context of video based behavior recognition.

ING

ML in predictive analysis, see link

Machine2Learn

Follow up of Scyfer. State of the art research on ML and DL with a practical compnent.

Xsens

ML and pose estimation and behavior modeling.

Scisports

ML and behavior recognition from sensors.

TNO Soesterberg

Projects on ML and BCI.

IBM

This assignment is related to the operational control of the service parts supply chain. Parts planners are making tactical decisions based on the actual status of the supply chain (actual stock, usage, demand patterns, network requirements). It is important that upcoming issues like understocking or overstocking are identified early, such that preventive actions can be taken. Other potential areas of proactive actions include (i) identification of de-lays in the physical distribution process having serious consequences, such that corrective actions can be invoked, (ii) reverse logistics, i.e. whether to return and repair failed parts that have been removed from the as-sets, and how to route them with which priority level.

Internal Master

PIHC project “A Healthy Brain in a Healthy Body”

For this project we are looking for students who can apply ML to the challenge of behavior recognition from smartphone and maybe smartwatch sensor data. The infrastructure for the sensing is provided by the HoliBehave platform. Moreover this platform should be extended to incorporate smartwatch data and experience sampling.