Clemens Stachl

Postdoctoral Researcher in Computational Behavioral Science at the Institute of Behavioral Science and Technology (IBT-HSG), University of St.Gallen, Switzerland. The ACTWELL project studies how subjective well-being is expressed in and shaped by everyday behavior. The work combines large smartphone sensing datasets from Europe and the United States with behavioral logs, contextual data, and self-reports. The successful candidate will coordinate research activities with Prof. Clemens Stachl, lead one or more work packages, and focus especially on sequence and dynamics analyses. Research tasks include processing and modeling high-dimensional intensive longitudinal smartphone log data, developing interpretable machine learning models linking behavior and context to well-being outcomes, publishing in psychology, computational social science, and interdisciplinary outlets, and presenting at international conferences. The role also includes maintaining reproducible analysis pipelines, preregistrations, and open-release materials in line with the SNSF Data Management Plan, plus contributing to privacy-preserving data handling, research ethics, and supervision of doctoral and master's students. Eligibility highlights: PhD in psychology, computational social science, data science, cognitive science, statistics, computer science, or a related field; strong applied machine learning skills; fluency in R and/or Python; experience with intensive longitudinal, sensing, or other high-frequency behavioral data; at least one first-authored empirical paper; excellent English; and a commitment to open, reproducible research. Funding and benefits: Competitive salary (F8), access to unique datasets, generous conference and research funding, international collaborations (including Stanford, LMU, and SODAS), mentoring for postdoctoral fellowships (SNSF, MSCA, Ambizione), relocation support, and hybrid work options. Application: Submit a single PDF containing a cover letter, CV with publication list, one first-authored paper or preprint, contact details of two referees, and PhD certificate or expected completion confirmation. Apply online using job ID 2803.