Assistant Professors in Statistics

Organisatie:
Department of Mathematics and Computer Science

Are you interested in developing novel statistical methods for relevant problems with complex, high-dimensional and network-structured data? Do you want to contribute to the high-tech systems sector, a core strength of the Brainport region and central to initiatives such as the Beethoven project? We are looking for three assistant professors, eager to expand our expertise in complementary and new directions.

These roles are primarily embedded in the Statistics Group , but possibly with strong synergies with the other groups as well. The statistics group focusses on the development of sound statistical models and methodology for the data-driven analysis of complex and dynamic systems, with the aim to perform predictive, prescriptive or preemptive analytics. We work together with external partners to actively secure funding driving innovation and growth.

Specifically, the research in the group revolves around two pillars: mathematical statistics and applied statistics, with ample space for projects that bridge and connect the two.

Our research activities are heavily intertwined with applications e.g., detection of anomalies, process monitoring and improvement, weather and climate forecasting, statistical bioinformatics. Topics are ranging from heavily data-driven to fundamental and methodological aspects, including causal learning, dependence structure models, high-dimensional and non‑parametric statistics, network statistics, data integration and functional data analysis. The group collaborates closely with the Teaching and Research Institute for Data Science Analytics (TRI‑DSA) .

We are looking for candidates with research profiles ranging from foundational to applied statistics addressing challenges arising in settings leading to highly complex data where standard statistical methods are inadequate. At the foundational level the ideal candidates should have a focus on the development of novel statistical methodology informed and inspired by rigorous performance analysis. On the applied level the focus should be on the development and use of statistical methodology in the context of, life‑sciences, environmental and industrial applications.

Relevant topics include:

• High‑dimensional statistics
• Network statistics and graphical models
• Causal inference and discovery
• Time series and functional data analysis
• Survival analysis
• Statistical data integration

As an ideal candidate, you should have a strong affinity with mathematics, coupled with an interest and drive for applications and openness to interdisciplinary collaboration. Expertise in applications relevant to the high‑tech systems sector --- a core strength of the Brainport region and central to initiatives such as the Beethoven project --- would be a highly valuable asset. You will contribute to the research themes within the statistics group (and those of the SPOR cluster at large) by developing novel complementary research lines and strengthening existing ones.

You are also expected to play a strong role in the educational programs in the department, where you will work with students from different backgrounds (mathematics, computer science, data science, and other engineers). Limited administrative tasks in the Department will also be part of the job.

• Motivated researcher, with a PhD in statistics, mathematics, or a closely related field. A few years of research experience after the PhD (e.g., postdoctoral experience) and international research experience is highly desirable.
• Ability to conduct high quality academic research, evidenced by peer‑reviewed publications in relevant, high‑ranked journals or conferences and invitations to speak at leading international conferences in the corresponding field.
• Vision and willingness to carve and pursue a research plan including supervision of PhD projects and committing to build strong external partnerships. Motivation to obtain funding from external sources to support innovative projects and drive impactful research.
• Strong affinity with real‑world applications.
• Motivated to teach, contribute to teaching processes, and to develop…