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University of Copenhagen
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Postdoctoral Fellowship in Causal Inference and Statistical Methodology University of Copenhagen in Denmark
Degree Level
Postdoc
Field of study
Biostatistics
Funding
This is a fixed-term, full-time postdoctoral position for two years, with the potential for up to one additional year to complete ongoing work. The average working week is 37 hours. The monthly salary starts at DKK 36,877.42, approximately EUR 4,933 at the August 2026 level, with a possible qualification supplement. The employer contributes an additional 18.07% to the pension fund. Eligible foreign and Danish applicants may qualify for tax reductions under the stated conditions.
Deadline
Dec 1, 2026
Country
Denmark
University
University of Copenhagen
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About this position
The University of Copenhagen invites applications for a postdoctoral fellowship in causal inference and statistical methodology within the Section of Biostatistics and the Pioneer Center for Statistical and Computational Methods for Advanced Research to Transform Biomedicine (SMARTBiomed).
The research focuses on the development of methods for longitudinal and time-to-event data, with particular emphasis on continuous-time causal inference, dynamic treatment strategies, identification, and instrumental variable methods. Potential directions include causal inference for survival and competing-risks outcomes, continuous-time longitudinal causal models, semiparametric and machine-learning-based estimation, treatment switching and discontinuation, dynamic treatment interventions, and efficient and robust estimators for complex longitudinal data structures.
The postdoctoral researcher will contribute theoretically and computationally by developing new methodology, studying statistical properties, implementing software, conducting simulation studies, and applying methods to biomedical research questions. Example projects include causal inference with continuously observed treatment processes, instrumental variables in survival analysis, and machine-learning-based estimation of time-varying treatment effects.
The fellow will join SMARTBiomed’s interdisciplinary research environment, which combines large-scale multimodal biomedical data with statistical, computational, and machine-learning innovation to advance the understanding, treatment, and prevention of human disease. The Section of Biostatistics provides an internationally recognized setting for research in time-to-event analysis, causal inference, latent-variable modeling, and prediction models, alongside teaching and biostatistical collaboration across health and medical sciences.
The position also involves leading research papers, presenting work at conferences, contributing to software and research-group activities, and developing independent research directions. Opportunities for graduate or undergraduate teaching and mentorship in developing new proposals and research initiatives are available.
Funding details
This is a fixed-term, full-time postdoctoral position for two years, with the potential for up to one additional year to complete ongoing work. The average working week is 37 hours. The monthly salary starts at DKK 36,877.42, approximately EUR 4,933 at the August 2026 level, with a possible qualification supplement. The employer contributes an additional 18.07% to the pension fund. Eligible foreign and Danish applicants may qualify for tax reductions under the stated conditions.
What's required
Applicants must hold a Ph.D. in Statistics, Biostatistics, or a similar discipline. Essential qualifications include strong skills in theoretical methods development, strong English scientific writing and oral communication skills, and the ability to work independently, effectively, and collaboratively. Experience and interest in data analysis and programming, such as R, are highly desirable.
How to apply
Submit an English-language online application through the Apply Now portal. Include a one-page motivation letter, CV, certified or signed PhD and MSc certificates (or a supervisor statement if the PhD is incomplete), and a publication list including archived works in progress, all in PDF format.
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