Postdoc Daytime at Eindhoven University of Technology

We are looking for a post-doctoral researcher at the Department of Computer Science and Mathematics of the Eindhoven University of Technology, the Netherlands in the fields of machine learning, knowledge mining & management and/or natural language processing. The researcher will work on the DayTime project.

Daytime stands for Digital Lifecycle Twins for Predictive Maintenance. The objective of the Daytime project is to demonstrate the applicability of Industry 4.0 and in particular DayTime innovations beyond traditional productions plants into the hospitals and the home by treating healthcare equipment as means or tools for production.

Healthcare encompasses both capital intensive equipment in hospitals and smart consumer products at homes. In either case reliable mapping of user interaction into system response is crucial in supporting the customer to optimally operate the product. DayTime will enable manufacturers to transform into digital service provides giving advice tailored to the user. This advice will be based on actual system status and usage combining digital twin concepts with user/usage profiling. The advice encompasses suggestions to improve performance, reduce wear and tear and provide instructions to improve longevity or pro-actively deliver maintenance as service.

The TU/e will focus on knowledge valorization by creating and translating research results into successful innovations, working together with the consortium partners, especially with Philips Research and Philips Magnetic Resonance business on the topics of predictive and reactive maintenance by applying data science and artificial intelligence technologies.

An extensive project description is available on request.

For more information about the project, please contact Prof. dr. Milan Petkovic, e-mail: m.petkovic@tue.nl

For information about employment conditions please contact Human Resources at TU/e, e-mail: pzwin@tue.nl

Please apply by using the ‘Apply for this job’ button. Amaximum of 5 documents of 2 MB each can be uploaded.