Postdoc position “Predictive Causal Relations in Complex Systems” at the Eindhoven University of Technology
We are looking for a postdoctoral researcher for the Primavera project (www.primavera-project.com), a national consortium involving several universities and industrial partners, revolving around data-analytics for predictive maintenance.
The postdoctoral researcher will work at the Eindhoven University of Technology (https://www.tue.nl), on a subproject that pertains specifically the development of novel methodologies to learn predictive causal models for complex systems from high-dimensional data, and use them to effectively monitor these systems. Learning such models from data is challenging, as these systems consist of many interconnected components, that interact between them in unexpected ways. Tackling this challenge requires sound use of multi-variate time-series methods endowed with proper complexity regularization, as well as the use of causal learning tools, such as transfer entropy and related approaches. In parallel to this the postdoctoral researcher will work on development of real-time monitoring and failure prevention methods based on these types of models.
Although the main goals are the development of methodology, the postdoctoral researcher will work in close collaboration with ASML – the world-leading manufacturer of integrated-circuit lithography machines. There will be ample opportunity to implement and test novel methodologies in a cutting-edge industrial research environment. This postdoc position offers a unique opportunity to work in the interface of academic and industrial research.
Applicants are required to have a PhD degree in mathematics, statistics or computer science, preferably in topics related to high-dimensional statistics, statistical learning and/or causal learning and inference. In view of the close collaboration with industrial partners, candidates should have interest in applied statistical research in an industrial context and have the proficiency necessary to implement and test novel computational methodologies. This position consists of full-time employment for 1 year with the possibility of an extension (up to a total of 3 years). For more information and for the application form see: