统计学博士研究员（含教学助理职责，3204 - 4051 欧元/月）

Are you our next PhD candidate in functional data analysis? Are you passionate about developing statistical methodology for complex, dynamic data? Join our team to develop methods for estimating time-varying network structures and detecting structural changes in functional time series.

Information In many scientific fields, observations are increasingly collected in the form of functions rather than simple numerical measurements. Examples include brain signals recorded continuously over time, and economic indicators observed as curves or distributions. Such functional time series often exhibit temporal dependence, non-stationarity, and complex non-Euclidean structures. These features contain valuable information, but they also make conventional statistical methods difficult to apply and can lead to unreliable conclusions.

Network structures provide a useful way to describe the relationships among the components of these complex data. In neuroscience, for example, connections between brain regions may be inferred from functional imaging signals rather than observed directly. Understanding which connections are present, how they evolve over time, and when structural changes occur is essential for scientific interpretation. Existing methods may produce false or missed connections when the observations are dependent, non-stationary, and non-Euclidean.

In this project, you will investigate theoretical tools for analysing dependence structures in functional time series. You will develop new methods and statistical softwares for constructing time-varying networks, detecting structural changes, and controlling false connections in complex settings. The proposed methods will contribute to deeper insights in distinguishing connections and changes from random variation in scientific studies. The PhD candidate will be supervised by Lujia Bai and Jeanine Duistermaat , and will publish in international scientific journals, participate in international conferences, and develop an independent research profile.

You will join the Statistics, Probability and Operations Research (SPOR) cluster in the Department of Mathematics and Computer Science at Eindhoven University of Technology. The cluster offers a collaborative research environment that connects time series analysis, functional data and network analysis. We work closely with external partners who drive innovation and growth, and our research is strongly embedded in applications such as anomaly detection, process monitoring and improvement, weather and climate forecasting, and statistical bioinformatics.

As an ideal candidate, you have a strong affinity for mathematical statistics and rigorous proofs, as well as a passion for developing and implementing novel methods and applying them to real-world data.