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The University of Copenhagen invites applications for a PhD fellowship in causal inference within biostatistical and epidemiological applications. The position is based at the Department of Public Health, Section of Biostatistics, and focuses on nonparametric causal inference and dynamic treatment regimes.
Application Deadline: April 1, 2026
Employment Start: July 1, 2026 (or as soon as possible thereafter)
Employment Duration: 3 years
Working Hours: Full-time
Project Overview
The PhD project aims to develop novel statistical methodology for causal inference in biostatistical and epidemiological studies, particularly focusing on longitudinal data with irregular or near-continuous monitoring.
Key goals include:
- Developing data-adaptive and semiparametric methods for dynamic treatment effect estimation
- Avoiding ad-hoc time discretization to better reflect clinical decision-making
- Strengthening methodological foundations for causal inference and improving reliability of evidence from observational healthcare data
Potential methodological themes:
- Dynamic treatment regimes
- Intensity-based or continuous-time interventions
- Semiparametric efficiency theory
- Targeted learning and doubly robust estimation
- Development and implementation of novel estimators
- Efficient testing of treatment effects
The project can follow a more theoretical path (continuous-time modeling, semiparametric theory) or a more applied/computational path (scalable algorithms, software development, clinical collaboration).
Supervisors
- Principal Supervisor: Associate Professor Helene Charlotte Wiese Rytgaard, Section of Biostatistics ([email protected])
- Principal Co-supervisor: Assistant Professor Anders Munch
- Co-supervisor: Assistant Professor Pawel Michal Morzywolek
Key Responsibilities
As a PhD student, you will:
- Conduct independent research under supervision
- Complete PhD courses (~30 ECTS points)
- Participate in active research environments, including research stays abroad
- Gain experience in teaching or other dissemination activities
- Write a PhD thesis based on your research
Required Qualifications
- Strong background in mathematics, statistics, computer science, physics, or engineering
- Documented skills in theoretical and applied statistics
- High-level programming proficiency
- Ability to communicate research effectively in teaching, conferences, and publications
- Must not already be enrolled as a PhD student at the Faculty of Health and Medical Sciences, University of Copenhagen
Employment Terms
- Full-time PhD fellowship for 3 years
- Conditional upon acceptance into the Graduate School at the Faculty of Health and Medical Sciences
- Salary: Approx. 29,691 DKK/month (~3,973 EUR) plus pension (November 2025-level)
- Employment follows Danish state agreements for academics
Application Requirements
Applications must be written in English and include:
- Motivation letter (max 2 pages): Previous research experience, interests, and motivation for this PhD
- Curriculum vitae: Education, work, research, teaching, conference presentations, publications, language skills
- Documentation of computational/statistical methods experience
- Official transcripts of examination results
- Certified copies of degree certificates
- Contact information for two references
Applications submitted after the deadline will not be considered.
Assessment Process
- Applications are evaluated by the hiring committee and unbiased assessors
- Applicants have an opportunity to comment on assessments related to themselves
- Interviews are expected to take place in week 19, 2026
- Assessment based on qualifications, research experience, technical skills, and potential
Working Environment
- Creative and stimulating international research environment at the University of Copenhagen
- Part of the Faculty of Health and Medical Sciences, with ~7,500 students, 1,500 PhD students, and 3,200 employees
- Opportunities for interdisciplinary collaboration and academic growth
- The University promotes an ambitious, informal, and collaborative work culture
Contact Information
Academic Contact:
- Associate Professor Helene Charlotte Wiese Rytgaard
- Email: [email protected]
This PhD fellowship provides an opportunity to advance cutting-edge methods in causal inference, bridging theoretical statistics, computational approaches, and practical applications in healthcare and epidemiology.
Apply now at: https://employment.ku.dk/phd?show=157207
