- The Internet of Things is changing the Earthmoving and Mining industry: remote condition monitoring is emerging and automation of up to 70% of today’s manual work can be achieved (McKinsey, 2017).
- Failure diagnostics of individual components is still in it‘s infancy, but is gaining in importance.
- This requires sophisticated machine learning techniques for analyzing huge amounts of sensor data.
- Within the thesis, students should prove the feasibility of cyber-physical system learning, using hydraulic seals in earthmoving and mining equipment as an example.
- Very good theoretical and practical understanding of machine learning, hands on experience in Python
- (Basic) electronics knowledge is a big plus
- Close mentoring from KSRI team and experts in machine learning, electronics, and hydraulics
- The student will (partly) work in the IoT lab in the recently opened Innovation Center in Stuttgart, including free high-quality coffee, fitness room, gaming room, and much more
- Start date: Flexible, October 2018 or later
If you are interested, send your CV, transcript of records and a brief description of your motivation to firstname.lastname@example.org.