PhD: Developing Methodological Standards for Digital Interventions
Develop novel statistical frameworks and optimize digital mental health interventions using micro-randomized trials.
Best for:Best for quantitative psychology or statistics graduates with a strong interest in digital mental health and advanced longitudinal data analysis.
Overview
This PhD position is offered jointly by the Methodology of Educational Science Research Group (CMPO) and the Center for Contextual Psychiatry (CCP) at KU Leuven. As part of the eMotion project, the successful candidate will focus on advancing statistical methodologies to optimize Ecological Momentary Interventions (EMIs) using Micro-Randomized Trials (MRTs) and virtual reality data. The role involves analyzing intensive longitudinal data, developing innovative quantitative frameworks, and collaborating with an interdisciplinary team of psychologists, statisticians, and psychiatrists. The researcher will be expected to publish findings in peer-reviewed journals and contribute to digital mobile mental health research.
What you'll do
- Apply advanced quantitative techniques, including time series analysis and multilevel modeling, to analyze Experience Sampling Method datasets.
- Design and develop novel quantitative frameworks to construct Ecological Momentary Interventions using Micro-Randomized Trials.
- Integrate virtual reality task data to enhance the effectiveness and evaluation of digital interventions.
- Author scientific papers for publication in international peer-reviewed journals.
- Collaborate across multidisciplinary research lines spanning ESM methodology, psychosis person-environment interactions, and digital mobile mental health.
Requirements
Degree required: Master’s degree in psychology, mental health, psychiatry, social sciences, health sciences, or a related discipline
Degree offered: PhD
Language: English; C1 proficiency level
Instruction: English
- Demonstrated strong interest in academic research and quantitative methodologies
- Solid practical experience with advanced statistical analysis and modeling
- Commitment to open-science principles and reproducible research practices
- Excellent written and spoken communication skills in English
- Holds a relevant master's degree prior to appointment
Funding & terms
How to apply
Deadline: 2026-12-07
Interested candidates should submit their application materials through the university's online job portal before the specified deadline.
- Cover Letter
- CV
- BSc Diploma and transcripts of records
- MSc Diploma and transcripts of records
- Contact details of at least two reference persons
- Publication list if applicable
Contact
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