You would join a team of postdoctoral researchers and PhD students working on physical activity and life satisfaction, affective states, cognitive abilities, and life outcomes. 

I would be very pleased to welcome you to the Uni-versity of St.Gallen soon. I am always happy to an-swer any questions you may have.

• Coordinate research activites in the ACTWELL project together with Prof. Stachl
• Lead one or more ACTWELL work packages, in particular the sequence and dynamics analyses
• Process and model high-dimensional, intensive longitudinal smartphone log data
• Develop and validate interpretable machine learning models linking behavior and context to well-being outcomes
• Publish as lead and co-author in psychology, computational social science, and
  interdisciplinary outlets; present at international conferences
• Maintain reproducible analysis pipelines and preregistrations, and prepare code and materials for open release in line with the project's SNSF Data Management Plan
• Contribute to privacy-preserving data handling and the lab's research ethics practice
• Co-supervise doctoral and master's students; contribute to lab meetings and methods workshops

• PhD in psychology, computational social science, data science, cognitive science, statistics, computer science, or a related field, completed by the start date or with the defence scheduled this year.
• Strong applied machine learning skills and fluency in R and/or Python
• Experience with intensive longitudinal, sensing, or other high-frequency behavioral data, and with the measurement problems these data raise
• Publication record appropriate to career stage, including at least one first-authored empirical paper
• Commitment to open, reproducible research
• Excellent written and spoken English

Postdoctoral Researcher – ACTWELL (SNSF Grant No. 215303)

The Computational Behavioral Science Lab at the University of St.Gallen, led by Prof. Dr. Clemens Stachl, is recruiting a postdoctoral researcher for the ACTWELL project.

ACTWELL investigates how subjective well-being is expressed in and shaped by everyday behavior. The project combines large smartphone sensing datasets from Europe and the United States with behavioral logs, contextual data (e.g. mobility, outdoor time), and self-reports. Using interpretable machine learning, we study how activities, contexts, and personal characteristics relate to life satisfaction and momentary well-being over time.

We offer: Competitive salary (F8), access to unique existing datasets, generous conference and research funding, international collaborations (Stanford, LMU, SODAS), a supportive research environment, mentoring for competitive postdoctoral fellowships (SNSF, MSCA, Ambizione), and relocation support with hybrid work options.

Application: Submit a single PDF including (1) cover letter, (2) CV with publication list, (3) one first-authored paper or preprint, (4) contact details of two referees, and (5) PhD certificate or expected completion confirmation.