Job Description

Institute of Education

Postdoc in Machine Learning and Interpretability for Cognitive Development 80 %

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Start of employment 1. November 2025 or upon agreement
The position is part of a joint project between two methodology oriented labs within Educational Science and Psychology.

Your responsibilities

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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 cap-ture learning trajectories, item characteristics, and contextual influences within large-scale cognitive develop-ment and educational testing datasets. They will drive methodological innovation by extending neural network approaches to integrate psychometric principles (for example, 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 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 education-al/psychological data science-for which additional remuneration is provided.

Your profile

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A PhD in psychology, statistics, computer science, or a related discipline Experience with psychological or related research Excellent methodological skills - machine learning and statistics, complex / 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

Information on your application

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To apply, please send a CV, motivation letter, contact details for 2 academic references, and sample of written work (not necessarily published) to Prof. Martin Tomasik in one single PDF until August 18 2025 : martin.tomasik@ife.uzh.ch. 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.

What we offer

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Work-Life Balance

Flexible working models (such as part-time positions, mobile working, job-sharing) Childcare at the kihz foundation of UZH and ETH

Learning and Development

Wide range of continuing education courses of UZH and the Canton of Zurich Language Center run jointly with ETH Zurich

Food

Food and drinks at reduced prices in the UZH cafeterias Lunch-Check-card with UZH contribution

Healthcare

Special conditions on the Academic Sports Association ASVZ Free seasonal flu vaccinations Rest and relaxation at the quiet room in the university tower

Discounts

Private traffic: Carsharing, rent a vehicle, parking space Digitalization: Hardware, software, mobile phone subscriptions Special conditions on hotel reservations

Conditions of Employment

Policies of the UZH Most UZH staff are employed according to public law

International Services

Support for people from outside Switzerland

Campuses

Campuses Zurich City, Zurich Irchel, Oerlikon and Schlieren Sites Zurich West, Old Botanical Garden, Botanical Garden and Lengg

Location

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Institute of Education

Kantonsschulstrasse 3, 8001 Zurich, Switzerland

Further information

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Questions about the job

Martin Tomasik

Professor of Research Methods in Developmental and Educational Sciences

Working at UZH

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The University of Zurich, Switzerland's largest university, offers a range of attractive positions in various subject areas and professional fields. With around 10,000 employees and currently 12 professional apprenticeship streams the University offers an inspiring working environment on cutting-edge research and top-class education. Put your talent and skills to work with us. Find out more about UZH as an employer!