PrimaVera is a major national project on data-analytics for predictive maintenance. We are looking for a postdoctoral researcher that will develop novel methodology needed to learn predictive causal relations in complex and high-dimensional systems, geared towards an exciting industrial context.
The postdoctoral researcher will work in the context of the PrimaVera project (primavera-project.com), a national consortium involving several universities and industrial partners. This prestigious project is one of the 17 projects of the funded by the Dutch Academy of Sciences in the context of the national Dutch Science Agenda.
Within PrimaVera, the postdoctoral researcher will work 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 multiple 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.
The statistics group at TU/e focusses both on theoretical and applied research. The research areas cover a wide range of topics: hypothesis testing (equivalence, non-inferiority), statistical process monitoring, survival and reliability theory, causality, mixed and latent variable models, non-parametric statistics, high-dimensional statistics, and statistical learning theory. The group actively explores new research lines in Data Science and has strong ties with industrial partners in various business domains, particularly the high-tech industry in the Brainport region around Eindhoven.
The statistics group is part of the SPOR (Statistics, Probability Theory and Operations Research) cluster within the TU/e subdepartment Mathematics. The SPOR cluster currently has 4 full professors, 6 part-time professors, 3 associate professors and 14 assistant professors. In addition, the cluster is strongly intertwined with EURANDOM, the European institute for research in Statistics, Probability and Stochastic Operations Research. EURANDOM is a workshop and visitor center that exists since 1998.
The Mathematics and Computer Science department consists of three charters: Mathematics, Computer Science, and Data Science. Knowledge valorisation within the department takes place through the Project Development Office (PDO). The department actively participates in EAISI, the Eindhoven Artificial Intelligence Systems Institute, a research institute covering many facets of data science and artificial intelligence. In the education and research areas, the department works closely together with other universities and with companies. TU/e is located in Brainport, an internationally renowned high-tech industrial area around Eindhoven with companies like Philips, ASML and NXP. Brainport also hosts the High Tech Campus, where 5000 industrial researchers from companies work together. This stimulating industrial environment enables TU/e to maintain close links with industry, healthcare and the building and logistics sectors.
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.
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