PhD student in epidemiology to develop and apply causal triangulation

Job description

Many key questions in epidemiology are fundamentally causal. Yet, we often have to rely on assumptions that cannot be directly verified to judge whether an observed association truly reflects causality. Causal triangulation offers a valuable tool to strengthen causal inference by comparing results from different study designs addressing the same question.

As part of this project, you will:

• Collaborate with our team to deepen the understanding of how causal triangulation works. This will rely heavily on causal inference but also formal reasoning and other methodological knowledge.
• In particular, this project will focus on how to quantitatively combine estimates from studies using different study designs.
• Help lay the groundwork for applying triangulation methods in practice (see DOI: 10.1007/s40471-023-00340-0).
• Apply triangulation to one or more concrete epidemiologic questions, such as the impact of cash transfers on child health — although we are open to other applications based on your interests.

You will also join a cohort of 8 PhD students across Erasmus MC, TU Delft, and Leiden University Medical Center, each focusing on different aspects of strengthening the safety and validity of causal inference in health research.

Work environment

As a PhD candidate, you’ll be based at Erasmus MC and become part of the Causal Inference Group; a lively community of students and faculty who meet regularly to exchange ideas, tackle methodological puzzles and explore new directions in causal research. It’s an inspiring, collaborative setting where there’s room to learn, share and grow together.

Your primary supervisor will be Dr. Jeremy Labrecque (scientific researcher). You’ll also benefit from the expertise and guidance of Dr. Jesse Krijthe at TU Delft and Dr. Stéphanie van der Pas at the Vrije Universiteit Amsterdam, who bring valuable perspectives and a strong network to your project. Together, this team offers a supportive environment to help you develop your skills and make an impact in the field.

Qualifications and skills

Some background in causal inference is a great advantage. There is room for learning on the job, but preference will be given to candidates who have taken courses or have demonstrated familiarity with at least the basics of causal inference:

• A Master’s degree in a quantitative research field (e.g., Biostatistics, Epidemiology, Economics, or Psychology).
• Strong ability to work collaboratively within a team, particularly within the Causal Inference Group and the Safe Causal Inference consortium.
• Proficiency in English (reading, writing, and speaking) sufficient to conduct and communicate scientific research.

Before you apply please check our conditions for employment.

Terms of employment

• You will receive a temporary position for 4 years.
• The gross monthly salary is € 3.108,- gradually increases to € 3.939,- in the 4th year (scale OIO) based on a full-time workweek of 36 hours.
• A salary increase of 3,5% from July 2026, according to the collective labor agreement (CLA)
• Partial working from home is possible, as is working 4 x 9 hours.
• Excellent fringe benefits, such as a 13th month that is already paid out in November and an individual travel expense package.
• An International Office which aids you in preparing for your arrival and stay.
• Pension insurance with ABP. We take care of approximately 2/3 of the monthly contribution.
• Special benefits, such as an incompany physiotherapist and bicycle repairer. And there is also a gym where you can work on your fitness after work. 

More information

Make this job your next career move! Apply now using the application button. It all starts with taking action.

Do you have questions about the role? Feel free to reach out to Jeremy Labrecque, Associate Professor, via mail.

No agencies please.