*Predict failures better and avoid train delays, power outages, and other failures? *Come join us and help realize the grand promises *if predictive maintenance*: Through an effective combination of sensor techniques, big data analysis, maintenance engineering, we want to predict failures better, so that maintenance can be more effective. The current position focus on an effective combination of formal methods (fault trees, stochastic model checking) and data analytics (decision trees, Bayesian Networks, neural networks) understand failure behaviour and it root causes better.
PrimaVera project is funded by the prestigious National Dutch Research Agenda. The project consortium is a team of leading researchers from Eindhoven University of Technology, Radboud University, Saxion, Haagse Hogeschool and the Dutch Aerospace Laboratory, as well as several industrial partners. The project funds 16 positions in total. *We seek *an excellent PhD candidate to work on the PrimaVera project with a strong background in theoretical computer science, or mathematics, and skills to apply theory in practice. You are expected to collaborate with the industrial partners on one or more case studies. *We offer *a fully paid PhD positions, with excellent salary and benefits, at a very strong and inspiring research department. To apply, see here <https://www.utwente.nl/en/organization/careers/!/988018/2-phd-position-on-the-primavera-predictive-maintenance-for-very-effective-asset-management-project> . Deadline: October 1. *We are *the Formal Methods & Tools at the University of Twente, the Netherlands. Ranked 1st in the last Dutch National Research Assessment. The project is led by Prof.dr.Marielle Stoelinga: [email protected] -- Prof.dr. Marielle Stoelinga Professor of risk management for high-tech systems University of Twente & Radboud University, the Netherlands +31 53 489 3773 | Address & contact <http://wwwhome.ewi.utwente.nl/~marielle/coordinates.html> | www.ewi.utwente.nl/~marielle/ <http://wwwhome.ewi.utwente.nl/~marielle/>
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