Key Skills

Job Description

About Your Role You will lead the development of a new sample size framework for nonrandomized studies that adjust for confounding. Such studies are essential when evaluating healthcare interventions that cannot be randomized for ethical or practical reasons. However, suitable methods for determining the required number of participants are currently lacking. Your research will focus on deriving and verifying new statistical methods and testing their usability in real-world studies. Working with clinical collaborators and a panel of end users, you will help ensure these methods meet the needs of researchers. Your Main Responsibilities Will Include Derive sample size formulas for different causal effects and methods of confounding adjustment. Conduct simulation studies to verify the accuracy of the developed formulas. Validate the framework when designing a prospective non-randomized study and organize meetings with a panel of end users. Contribute to scientific publications, oversee the development of an open-source R package, and contribute to teaching activities. The position is for 24 months. You will work closely with a software developer who will implement your methods in an open-source R package. Throughout the project, you will have the freedom to shape the methodological approach, with regular input from your supervisors and international collaborators from the STRATOS Initiative's Causal Inference topic group. You will also present your findings at national and international conferences. We support your professional development, providing opportunities to explore your own research ideas and work towards follow-up projects and funding applications. About Your Workplace You will work within the Department of Biomedical Data Sciences at the LUMC, which develops, interprets, evaluates and implements advanced statistical methods and data science techniques for (bio)medical research. Your daily workplace will be the Medical Statistics section, which hosts the largest biostatistics group in the Netherlands. Your project will be embedded in the Caecilia Causal Inference Center, a cross-departmental expertise center for causal inference within the LUMC. You will collaborate closely with clinical partners from the departments of Gynecology at the LUMC and Erasmus MC, who will bring in real-world research questions and data. You will also have access to an international network of causal inference experts through the Causal Inference topic group of the STRATOS Initiative. The project is led by Dr Doranne Thomassen, Dr Nan van Geloven and Prof. Dr Saskia le Cessie, who will supervise you. About You Developing statistical methods for causal inference appeals to you, particularly when your quantitative skills can contribute to improving healthcare. You enjoy exploring complex methodological questions and explaining your findings to others, including clinicians and researchers from different scientific backgrounds. Within an international team of biostatisticians and clinicians, you take initiative, share ideas and collaborate effectively. In Addition, You Have A PhD in (Bio)Statistics, Applied Mathematics, Health Data Science or a related field. A track record in developing statistical methodology, preferably in causal inference. Experience deriving theoretical properties of statistical estimators and testing them through simulation studies. Programming skills, for example in R. Excellent communication skills in written and spoken English (C1 level or higher). Experience with sample size calculations for clinical studies is an advantage. Speaking Dutch is beneficial but not required. Our offer Driven by health; that is our mission. This applies not only to our patients but also to our employees. We strive to create a safe, inclusive and equitable working environment where talent development and supportive leadership are central. To encourage continuous learning and professional growth, we offer internal training and development opportunities. In addition, you will benefit from an annual year-end bonus (8.3%), holiday allowance, a bicycle scheme, a sports budget, a working-from-home allowance and an excellent commuting allowance. As an LUMC employee, you will also participate in the ABP pension scheme. LUMC pays 70% of your pension contributions, leaving you with more net salary. See all employment conditions of the LUMC. What do we represent? As an academic medical center, LUMC strives to build a workforce that reflects the diversity of society. We are committed to delivering the highest quality in healthcare, education and international research, recognizing that diverse perspectives are essential to achieving this. Sustainability is also a key priority. We work towards a healthier future, not only for people but also for the planet. In line with these values, we aim to create a creative and inspiring workplace where everyone feels welcome, safe and valued. What matters to us is who you are and what you contribute, regardless of your background. Together, we are building a sustainable and inclusive future for the people around us and the world we live in. Together, we are LUMC. More information The salary for this position ranges from € 3,724 to € 5,867 gross per month (CAO UMC), based on full-time employment. This is a fixed-term position of 36 hours per week for 24 months. A salary increase of 3.5% will take effect on 1 May 2027. Interviews are scheduled for the week of November 30, 2026, primarily on December 1 and 3. Please keep these dates in mind when applying. This vacancy is open to both internal and external candidates. In accordance with LUMC policy, internal candidates will be given priority if equally qualified. Recruitment agencies are kindly requested not to respond to this vacancy.

Core Responsibilities

Develop and derive sample size methods for nonrandomized studies with confounding adjustment, and verify their accuracy through simulation studies. Validate the framework in a prospective study, collaborate with clinical partners and end users, contribute to publications and teaching, and oversee implementation in an open-source R package.

Requirements

A PhD in (Bio)Statistics, Applied Mathematics, Health Data Science, or a related field is required, along with experience developing statistical methodology and deriving and testing estimator properties. Applicants should have programming skills such as R and excellent written and spoken English; clinical-study sample size experience is an advantage, and Dutch is beneficial but not required.

Benefits

• Year-End Bonus
• Holiday Allowance
• Bicycle Scheme
• Sports Budget
• Work-From-Home Allowance
• Commuting Allowance
• ABP Pension Scheme
• Training And Development Opportunities