Postdoc in Machine Learning and Interpretability for Cognitive Development

We are seeking a highly motivated postdoctoral researcher to join an interdisciplinary project at the intersection of educational science, psychology, and quantitative methodology. The successful candidate will play a central role in an innovative collaboration between two methodologically focused labs at the University of Zurich, contributing to cutting-edge research on cognitive development and learning using advanced neural network approaches.

Responsibilities

The successful applicant will collaborate closely with Prof. Dr. Charles Driver, head of the Quantitative Methods unit (Psychology), Prof. Dr. Martin Tomasik, head of the Research Methods unit (Educational Science), and the broader research team. Although the position is primarily research‐oriented, there is potential involvement in teaching, statistical consulting, and supervision.

As scientific lead of a cross‐institute workgroup, the candidate will develop, train, and compare modern neural network architectures—such as graph neural networks, recurrent neural networks, and hybrid models—to capture learning trajectories, item characteristics, and contextual influences within large‐scale cognitive development and educational testing datasets. They will drive methodological innovation by extending neural network approaches to integrate psychometric principles (e.g., item response theory), predict individual change over time, and distinguish item properties.

In addition, the successful applicant will disseminate findings through high‐impact journal articles, presentations at international conferences, and by contributing to open‐source code. They will also co‐supervise doctoral and master’s students, coordinate regular lab meetings, and foster an inclusive, collaborative culture. Optionally, the appointee may teach up to 2 SWS per semester in quantitative methods, machine learning, or educational and psychological data science—for which additional remuneration is provided.

Requirements

• A PhD in psychology, statistics, computer science, or a related discipline
• Experience with psychological or related research
• Excellent methodological skills—including machine learning, statistics, and complex or longitudinal data structures
• Proficiency in programming language/s (e.g., R, Python, Julia, C++)
• Very good command of English as the work language
• Proven experience with publishing in international journals

Start of employment

August 1, 2025 or upon agreement

Employment level

80%

Life in Zurich

Living in Zurich offers an exceptional quality of life, combining natural beauty, safety, and modern infrastructure. The city consistently ranks among the world's most livable, with excellent public transportation, clean and efficient services, and abundant green spaces, including lakeside and mountain access. Zurich also boasts a vibrant cultural scene, diverse international community, and top-tier healthcare and education systems.

Application procedure

To apply, please send a CV, motivation letter, contact details for 2 academic references, and a sample of written work (not necessarily published) to Kristina Mink (kristina.mink@ife.uzh.ch) in one single PDF on or before June 26, 2025 (note that interviews may commence earlier).

Informal questions are welcome and may be directed to either Prof. Dr. Charles Driver (charles.driver@psychologie.uzh.ch) or Prof. Dr. Martin Tomasik

(martin.tomasik@ife.uzh.ch) or both. Late applications may be considered. A similar position but for PhD / doctoral candidates might also be available.