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.
Requirements
Specific Requirements
- A master’s degree in Statistics, Mathematics, Econometrics or related majors.
- Motivated to develop your teaching skills and coach students.
- Fluent in spoken and written English (C1 level).
- Good programming skills preferably in R or Python. Experience in programming with C++.
- Prior knowledge in time series analysis, functional data analysis or functional analysis is of benefit.
- Excellent communication skills.
Additional Information
Benefits
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station.
In addition, we offer you:
- Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
- Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,204 - max. € 4,051 gross base salary per month (full-time)).
- In addition to your base salary, you will receive an 8% holiday allowance and an 8.3% year-end bonus, both calculated based on your annual gross base salary.
- Generous leave options: a standard 29 days (based on a 38 hour working week) per year that you can increase to 41 days by working two hours more per week (flexible working hours). This is prorated if you work part-time.
- As a TU/e employee, you participate in the ABP pension scheme, providing retirement pension and pension benefits for surviving dependents and occupational disability. TU/e pays 70% of the pension premium, while employees contribute the remaining 30%.
- High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
- An excellent technical infrastructure, and on-campus children's day care.
- Unlimited access to the modern on-campus TU/e Student Sports Center at an exceptionally affordable rate.
- We support your wellbeing with free 24/7 access to OpenUp, providing you and your family with mental health support, expert guidance, and online training.
- An allowance for commuting, working from home and internet costs.
- A Staff Immigration Team and a tax compensation scheme (the Expat Scheme) for international candidates.
On our website you can discover even more information about our conditions of employment. Build on your career at TU/e!
Selection process
We invite you to submit a complete application by using the apply button. The application should include a:
- Cover letter in which you describe your motivation and qualifications for the position.
- Curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.
Ensure that you submit all the requested application documents. We give priority to complete applications.
We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.
Additional comments
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Prof. dr. Jeanine Duistermaat, j.j.duistermaat@tue.nl.
Visit our website for more information about the application process. You can also contact HR services M&CS, hrservices.mcs@tue.nl.
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