项目介绍

2026年7月23日

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.

Your profile

• You have, or will shortly acquire a master’s degree in (applied) mathematics, statistics, or a related degree with a strong theoretical mathematical component (including probability theory and mathematical statistics). Enthusiasm for the topic of the project is more important than your choice of specialisation in your master’s.
• You enjoy scientific discussions and gatherings, and you want to develop your academic profile in mathematical statistics.
• You are proficient in English.
• Good scientific programming skills, e.g. in R, Python, C++, Julia, etc.

Our offer

• As a PhD candidate at UT, you will be appointed to a full-time position for four years, with a qualifier in the first year, within a very stimulating and exciting scientific environment;
• The Statistics group offers a dynamic ecosystem with enthusiastic colleagues;
• Your salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAO-NU);
• You will receive a gross monthly salary ranging from € 3.059,- (first year) to € 3.881,- (fourth year);
• There are excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• The flexibility to work up to two days per week from home;
• A minimum of 232 leave hours in case of full-time employment based on a formal workweek of 38 hours. A full-time employment in practice means 40 hours a week, therefore resulting in 96 extra leave hours on an annual basis.
• Free access to sports facilities on campus
• A family-friendly institution that offers parental leave (both paid and unpaid);
• You will have a training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• We encourage a high degree of responsibility and independence, while collaborating with close colleagues, researchers and other staff.

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.Apply now