Postdoc in Non-random selection and causal inference at the Department of Public Health

The Section of Biostatistics is looking for a postdoc to develop statistical methods for inference on causal effects in studies affected by non-random participation, particularly self-selection in cohort studies. The position is for 3 years, starting on October 1, 2026, or as soon as possible hereafter.

Information on the Department of Public Health can be found at https://publichealth.ku.dk/

Our research
Embedded within the Section of Epidemiology, Department of Public Health, University of Copenhagen, the Social Magnifier research group focuses on selection in cohort studies and aims to advance methods to account for it. Specifically, the group investigates how socioeconomically skewed participation, that is expected to be magnified by differential attrition with increasing age, risks distorting the understanding of inequalities in health. Another focus area is the impact of incentives to increase participation on selection patterns. The research group is led by Professor Katrine Strandberg-Larsen and funded by the Carlsberg Foundation.

SMARTbiomed is a research center with core mission to develop statistical and computational methods focusing on causal inference, risk prediction and machine learning. SMARTbiomed has hubs at Aarhus University, Copenhagen University and University of Oxford that offer a vibrant international community of researchers, providing an exciting environment of collaboration to attract early-career researchers from a wide variety of fields, working as a team towards unified goals. You will be based at the causal inference hub at the Section of Biostatistics, University of Copenhagen, led by Professor Erin Gabriel. SMARTbiomed is funded by the Danish National Research Foundation, the Novo Nordisk Foundation, the Lundbeck Foundation, the Carlsberg Foundation and the Villum Foundation. Information on the center can be found at: https://smartbiomed.dk/

Your job
You will be a member of and engage in collaboration with the SMARTbiomed Causal Inference and Social Magnifier research environments. Motivated by empirical evidence that participants who remain in cohort studies over time tend to be more socioeconomically advantaged, the Social Magnifier group examines the consequences of selective attrition for the study of social disparities in health as well as methodological strategies to address them. The cohort studies considered combine self-reported data with individual-level register data from the Danish national registers. While register data is available for all individuals invited to participate, self-reported information (e.g., outcomes, exposures and/or confounders) are only observed for participants. Differential non-random self-selection occurs both at the initial enrolment stage and throughout successive waves of data collection. Some studies implement incentives to increase participation. You will develop methodology for learning causal effects in the referent population, leveraging the availability of comprehensive register data. There is also an interest in methods for transporting effects from historic cohorts to contemporary populations.

Your specific tasks will include:

• Conducting research on statistical methods within the areas described above.
• Leading write-up of research manuscripts based on your own work and collaborating on joint research publications. Ensuring, where appropriate, that software outputs are provided to ensure maximum utility.
• Presenting papers at conferences or public meetings.
• Contributing to and supporting the collaborative research infrastructure of the Social Magnifier and SMARTbiomed environments.

You will also be encouraged and mentored to develop new proposals and research initiatives to simultaneously support an individual research profile. Over the course of the position, you will participate in the daily duties of the department, such as teaching at the graduate and/or undergraduate level.

Profile
Essential experience and skills:

• You have a PhD in in Statistics, Biostatistics, or a similar discipline.
• You have strong skills in theoretical methods development.
• You have proficient communication skills and ability to work both independently and collaboratively in teams.
• You have excellent English skills written and spoken.

Desirable experience and skills:

You have experience and active interest in

• data analysis
• register data
• programming (e.g., in R)

Place of employment
The place of employment is at the Section of Biostatistics, Department of Public Health, University of Copenhagen. The section carries out independent research in biostatistical applications and methods; it provides both undergraduate as well as postgraduate teaching and offers statistical advice to staff and students in all disciplines of the Faculty of Health and Medical Sciences. The section is internationally renowned for a high research level, especially within time-to-event analysis, while other competence areas include e.g. causal inference, latent variable modelling and prediction models. The section aims to maintain a strong position in these areas but also wants to expand into new areas of biostatistical research. The overarching goal is to produce high-level methodological research that is relevant in practical applications. More information on the Section of Biostatistics can be found at http://biostat.ku.dk.

Terms of employment
The average weekly working hours are 37 hours per week.

The position is a fixed-term position limited to a period of 3 years. The starting date is October 1, 2026, or after agreement.

Employment will be in accordance with the provisions of the collective agreement between the Danish Ministry of Finance and AC (the Danish Confederation of Professional Associations). The monthly salary will be based on the number of years of work experience (seniority) with the possibility to negotiate a salary supplement based on prior experiences and qualifications. The employer will pay an additional 18,07% to your pension fund.

Foreign and Danish applicants may be eligible for tax reductions, if they hold a PhD degree and have not lived in Denmark the last 10 years.

The position is covered by the Job Structure for Academic Staff at Universities 2020.

Questions
For further information please contact Frank Eriksson eriksson@sund.ku.dk

Foreign applicants may find this link useful http://www.ism.ku.dk (International Staff Mobility).

Application procedure
Your online application must be submitted in English by clicking ‘Apply now’ below. Furthermore, your application must include the following documents/attachments – all in PDF format:

1. Motivated letter of application (max. one page).
2. CV incl. education, work/research experience, language skills and other skills relevant for the position.
3. A certified/signed copy of a) PhD certificate and b) Master of Science certificate. If the PhD is not completed, a written statement from the supervisor will do.
4. List of publications.

Deadline for applications: 5 July 2026, 23.59pm CET

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process
After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the hiring committee. All applicants are then immediately notified whether their application has been passed for assessment by an unbiased assessor. Once the assessment work has been completed each applicant has the opportunity to comment on the part of the assessment that relates to the applicant him/herself.

You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/

The applicant will be assessed according to the Ministerial Order no. 242 of 13 March 2012 on the Appointment of Academic Staff at Universities.

Interviews are expected to be held in week 34 and 35.

The University of Copenhagen wish to reflect the diversity of society and encourage all qualified candidates to apply regardless of personal background.