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Rijksuniversiteit Groningen
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The experience of stress is an inherent aspect of daily life. However, can we validly measure this, and how and under what circumstances does it contribute to disease? We at the department of Psychometrics and Statistics at the University of Groningen are looking for a PhD candidate to work with us on the ambitious Stress in Action project (more details about the project can be found here: https://stress-in-action.nl/).
Where are you going to work?
The position is situated in the Faculty of Behavioral and Social Sciences at the statistics and psychometrics department (https://www.rug.nl/research/heymans-institute/research-units/psychometrics-and-statistics/) and the LaBlab (https://www.laurabringmannlab.com/about).
Furthermore, as the position is part of an interdisciplinary project, you will be also working with the VU Amsterdam in Prof. dr. Hoogendoorn’s lab (https://www.few.vu.nl/~mhoogen/). Thus, short visits to Amsterdam are to be expected.
What are you going to do?
The central aim of this project is to contribute to the data-analytic goals of the Stress in Action programme by comparing and integrating machine learning (ML) approaches with dynamic intensive longitudinal data (DILD) modelling. As part of the Data Analytic Support Core (DASC), this project focuses on how the dynamic interaction between contextual stress exposure and multicomponent stress responses in daily life can be best modelled, while accounting for individual differences.
A key innovation is the deliberate combination of ML and time-series modelling techniques. Whereas ML methods are typically optimized for data reduction and prediction, and DILD approaches emphasize explanation and interpretability of temporal processes, their integration is expected to yield both accurate predictions and a deeper understanding of what these predictions represent in daily life.
A central focus of the project is the role of qualitative data. Qualitative information will be used to contextualize and interpret quantitative patterns, helping to clarify how participants experience, interpret, and respond to stress in daily life. In addition, qualitative insights will inform model development by identifying meaningful features and improving the ecological validity of predictions.
Finally, the project will examine how much data is required to obtain reliable and meaningful predictions. This involves combining quantitative evaluations of model performance with qualitative approaches to understand, in practice, how many data points are needed to make valid inferences about stress processes in everyday life.
Who are you?
- Research MSc degree (or finishing soon) in psychology or a related discipline.
- Experience in collecting Experience Sampling Methods data.
- Knowledge of multilevel models, Large Language Methods and machine learning approaches.
- Experience with time series analysis and structural equation modelling.
- Programming skills in R.
- Previous experience with working with qualitative data is also a plus.
- Active knowledge of Dutch (level A2) in order to work with the qualitative data.
- Affinity and preferably experience with writing research papers.
- Good social and communication skills and willing to work with other team members.
- Prior teaching experience, preferably in statistical courses.
- Enthusiastic about translating scientific insights into practical guidelines and advice.
- Demonstrable competences such as conceptual capacity, presenting, planning and organizing and monitoring.
