Institute of Education
Postdoctoral Researcher in Modeling Intraindividual Learning Trajectories with Intensive Longitudinal Data 70-90 %
The Institute of Education at the University of Zurich is accepting applications for a Postdoctoral Researcher for modeling intensive longitudinal educational testing data. The work load is negotiable and the initial appointment will be until February 29, 2024. The position primarily involves work in the methodological research project “Modeling Developmental Trajectories with Intensive Longitudinal Data from Large-Scale Formative Assessments” funded by the Swiss National Science Foundation (PI: Prof. Dr. Martin Tomasik). It is a full research position with no teaching obligations, but teaching con-tracts can be negotiated to temporarily or permanently complement a part-time employment in the project.
The successful candidate will collaborate in a team comprising another post-doctoral researcher (Dr. Charles Driver) and a PhD student. Against the backdrop of intensive longitudinal data from a computer-based tool for formative student assessment (see Tomasik, Berger & Moser, 2019, in Frontiers of Education), the successful candidate will apply and develop methods for modelling intraindividual learning trajectories and assessment characteristics across different subject domains, investigate patterns of heterogeneity in these trajectories, study the longitudinal dynamics between concepts and content domains, and investigate contextual factors predicting learning trajectories. A significant portion of the working time will be devoted to prepare the results of this modelling for publication in scientific journals and presentation at international conferences. The successful candidate might also be involved in mentoring a doctoral student.
Qualified candidates should be self-driven and highly motivated individuals with an established track record, including first-author publications in top-tier international journals. They should have a good statistical and/or computational background in order to apply and contribute to development of methods such as item response theory, time-series analysis, dynamic systems, growth curve modelling and/or other related methods capable of separating between-person and within-person variance, in order to study interindividual differences in intraindividual change. A solid knowledge of item response theory, good command of machine learning approaches, and programming skills (e.g., R, Python, C/C++, MATLAB) are a strong advantage. The successful candidate should have obtained a doctoral degree in psychology, mathematics, statistics, computer science, psychometrics, developmental measurement or educational measurement no longer than seven years ago and bring along some experience – or at least strong interest – in developmental and/or educational research. A good command of English and/or German (as the working languages in the project) is mandatory, familiarity with any of the other national languages (i.e., French and Italian) could be helpful.
What we offer
We offer great working conditions on a unique data set (N ˜ 100.000) and the opportunity to significantly expand one's publication record. This interdisciplinary research project is conducted in collaboration with leaders in the field of machine learning, signal processing theory, computational social science, and data analytics. The University of Zurich provides great opportunities for networking within and beyond the Digital Society Initiative (see website). The Institute offers an attractive salary plus a generous social benefits package, five weeks of paid vacation per year, flexible working hours as well as a highly motivated and supportive team of scientific and non-scientific personnel.
Place of work
Freiestrasse 36, 8032 Zürich
Start of employment
Emploment start date to be negotiated
Dr. Charles Driver
Die Ausschreibung ist auch im Internet unter https://jobs.uzh.ch/offene-stellen/postdoctoral-researcher-in-modeling-intraindividual-learning-trajectories-with-intensive-longitudinal-data/9c5643d4-2e93-4639-8d78-e9283926f460 abrufbar.