In this project, we develop a new mathematical framework to efficiently model multivariate systems during extreme events like floods, heatwaves, or financial crashes. This PhD position offers a combination of theoretical research and the implementation of new statistical methodology and thus requires good knowledge of probability theory and mathematical statistics.
Many real-world applications exhibit cause-effect relations, where each variable in a system can be considered as an imperfect reflection of the influences of related variables. Structural causal models provide a flexible mathematical framework for efficient prediction, model selection and management in such settings. However, when interest is rather in the behavior of the system during extreme events, for example during floods in a river network or crashes of the stock market, current approaches for structural causal modeling in extremes are limited to scenarios where all variables are simultaneously large. Such assumptions are often unrealistic, and can therefore result in inadequate models. This project proposes a generalized framework for extremal structural causal models on arbitrary directed acyclic graphs. Our new models will be able to incorporate non-standard extreme directions, which permits the modeling of settings where only parts of a system are extreme. Special attention will be given to parametric families like the Hüsler–Reiss distribution, which lead to an extremal analogue of Gaussian structural causal models. We further propose to develop scalable structure learning methods for these new models, including latent variable identification, and to demonstrate their effectiveness on real data. For this, we will study the model class of linear non-Hüsler–Reiss acyclic models and their generalization that includes latent variables. Our proposal will fill a critical gap in statistical methodology, offering robust tools for prediction and management of extreme events in high-dimensional, directed systems.
The project is funded by an M1 Open Competition grant from the Dutch research council (NWO). The successful candidate will be appointed in the Statistics group at the University of Twente, mainly supervised by Dr. Frank Röttger. The position comes with funding for active participation in scientific workshops and conferences, and includes research stays with international collaborators.
We will continually review the applications, and as soon as we find a suitable candidate, we will arrange an interview. We therefore recommend to apply at your earliest convenience. The starting date can be agreed upon after the interview, but will preferably be between October 2026 and early Spring 2027.
Your tasks:
- Perform daily PhD-level research.
- Publish results in journals and conference proceedings, and present these at (inter)national workshops and conferences.
- Contribute to teaching activities related to your work (at most 10% of your time).
- Actively participate in seminars, scientific discussions, and contribute to the good atmosphere in the group.
- Be a part of an excellent young research group.
We are an inclusive group and diversity is at the heart of our research principles. We care about a good working atmosphere and a good work-life balance. Applications from all groups currently under-represented in academic posts are especially encouraged. We particularly encourage women to apply.
Information and application
Are you interested in this position? Please send your application via the 'Apply now' button below before September 7, 2026, and include:
- A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications and references.
- A cover letter (maximum 2 pages A4), emphasising your specific interest, qualifications, and motivations to apply for this position.
- Contact information of two references whom we may contact.
For more information regarding this position, you are welcome to contact (Frank Röttger, f.rottger@utwente.nl)
We will continually review the applications, and as soon as we find a suitable candidate, we will arrange an interview. We therefore recommend applying at your earliest convenience.
Screening is part of the selection process.
About the department
Statistics Research Group: The research focus of the statistics group is on the development of statistical methodology for new data applications and the theoretical analysis of machine learning methods. A list of members of the statistics group can be found via this link. The statistics group is embedded within a larger data science initiative at the University of Twente’s Department of Applied Mathematics.
Department of AM:
The Department of Applied Mathematics has as objective to develop mathematics in the context of important societal problems. We do fundamental research and also encourage and support interdisciplinary collaborations. In applications we focus on systems that are crucial to every-day life. We contribute to smart grids that make energy networks more efficient, mathematical models that assist medical doctors, schedules that make hospitals more efficient and numerical schemes to study multiscale fluid problems and wave propagation from nano to kilometer scales.
About the organisation
The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology (ICT). With ICT present in almost every device and product we use nowadays, we embrace our role as contributors to a broad range of societal activities and as pioneers of tomorrow's digital society. As part of a tech university that aims to shape society, individuals and connections, our faculty works together intensively with industrial partners and researchers in the Netherlands and abroad, and conducts extensive research for external commissioning parties and funders. Our research has a high profile both in the Netherlands and internationally. It has been accommodated in three multidisciplinary UT research institutes: Mesa+ Institute, TechMed Centre and Digital Society Institute.
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