PhD Candidate Optimal Design and Principled Statistical Analysis of SEM-Based fMRI Connectivity

Welcome to Maastricht University! 
Are you interested in statistics and sample size calculations for fMRI research? Join us to develop simulation tools and power analysis methods for fMRI connectivity studies using Structural Equation Modeling!

PhD candidate Optimal Design and Principled Statistical Analysis of SEM-Based fMRI Connectivity - Through Realistic Simulation

• Our goal: this project develops statistical methods to evaluate connectivity models and optimize study design. You will extend a R program to simulate realistic fMRI datasets and build tools for power and sample size analysis. The goal is to enable more reliable, efficient, and scientifically rigorous fMRI research.
• Your colleagues: you will work in the FHML division of the interfaculty Department of Methodology and Statistics (M&S) at FHML and the Faculty of Psychology and Neuroscience. With around 20 staff members, M&S provides statistics education across multiple Bachelor, Master, and PhD programs. The department also offers statistical support for researchers from both faculties. Initially focused on study design and analysis methods for nested and longitudinal data, M&S now includes Bayesian statistics, multilevel interrater agreement, missing data methods, latent two-mode interaction, structural equation modelling, and causal inference. Visit the M&S page

In this project you will develop statistical methods and software tools to support the design, planning, and analysis of fMRI studies, including connectivity analysis and activation modeling. Specifically, you will extend the R package neuRosim to simulate realistic fMRI data with more accurate spatial and temporal noise structures, bringing simulations closer to real acquisition conditions. You will use these simulations to evaluate and compare connectivity models, including among others SEM-based approaches and Bayesian statistical techniques, and develop power analysis and sample size calculation methods for fMRI study designs. The result will be a principled framework that helps researchers design statistically valid and cost-efficient fMRI studies, advancing good research practices in the neuroimaging field.

What you do

In this project, your responsibilities will entail:

• Conduct a systematic review of fMRI simulation tools, connectivity modeling approaches, and current practices in power analysis and sample size determination.
• Extend the R package neuRosim with more realistic spatial and temporal noise structures, validated against empirical fMRI data.
• Evaluate and compare SEM-based and network-based connectivity models under realistic simulation conditions, incorporating Bayesian statistical techniques.
• Develop power analysis and sample size calculation methods for fMRI study designs.
• Integrate simulation, and optimal design principles into a user-friendly R-based software package.
• Present research at conferences and publish results in international methodological journals
• Do some teaching (maximum 0.1 FTE).

Are you ready to set the course for the years ahead? Then we’d love to meet you. 

What you bring 

We’re not looking for checkboxes; we’re interested in who you are and what you bring. Do you recognize yourself in this?

You take initiative, are creative, and possess strong analytical skills. You work in an organized and focused manner. In addition, you are someone who can work independently while also collaborating effectively within a research team. Furthermore, you bring:

• Master's degree in Statistics, Mathematics, Physics, Econometrics, Biostatistics, Psychometrics, or a related quantitative field.
• Excellent statistical programming skills in R, including experience with simulation, as demonstrated by your master thesis or other scientific work.
• Solid background in statistical modeling, including structural equation modeling or related multivariate methods.
• You understand the basic characteristics of fMRI methodology or are motivated to learn them quickly; prior experience is an asset.
• Experience with Bayesian statistics and Bayesian computational tools is a plus.
• Experience with evaluating statistical power, sample size, or design efficiency in complex models is a plus.
• Excellent English communication skills (both verbally and written). 

What we offer 
At Maastricht University, you’ll work in an international, open, and engaged environment. We offer: 

• A 12-month contract (1,0 FTE) with the prospect of a 3-year extension.
• A gross monthly salary between €3.059 and €3.881 (based on full-time employment of 38 hours per week). 8% holiday allowance and an 8.3% year-end bonus.
• 29 vacation days (based on full-time), four additional days off (Carnival Monday and Tuesday, Good Friday, and Liberation Day), and the possibility to accrue up to 12 extra days through compensation hours.
• Flexible working hours, a home office allowance, and the option to work from home.
• Freedom and space to shape your work independently and develop your ideas.
• A close-knit community of colleagues to collaborate and grow with.
• A solid pension plan via ABP, company fitness schemes, and access to various university sports facilities.
• An inspiring work environment in the heart of Europe. 

About the Faculty of Health, Medicine and Life Sciences (FHML)
FHML is committed to health in the broadest sense: from molecule to human, and from healthcare to prevention. We train healthcare professionals and researchers through innovative educational programmes and conduct groundbreaking research in health and well-being. As part of Maastricht UMC+ (MUMC+), our international and interdisciplinary community forms a unique collaboration between university and academic hospital, where education, research, and care come together.

Interested? 
Want to know more about this position or what it’s like to work at our university? Reach out to Dr. Benedikt Langenberg at Benedikt.Langenberg@maastrichtuniversity.nl for more information. The end date for application is 21 June 2026. 

Please upload your motivation letter, CV (also mention the topic of your master thesis), grade list of your bachelor and master studies, and, if possible, contact information of two referees. Please upload all of these (in that order) in one PDF document.

The first interviews take place between 25 June 2026 and 3 July 2026. The selection process may include an assignment and/or a presentation.

The starting date of the position is flexible, but preferably before 1 September 2026.

Apply now via the button below. We look forward to getting to know you!

About Maastricht University
At Maastricht University, we collaboratively seek solutions to help move the world forward. We do this with 23,300 students and 5,400 employees across 5 regional locations, 6 faculties, and more than 70 research institutes. We encourage you to push boundaries and discover new opportunities for yourself and the world around you. Together, we can find the answers for tomorrow. 

The vacancy is open for internal and external candidates. In case of equal qualifications, internal candidates will be prioritized.

At Maastricht University, we prefer to contact potential candidates directly. We therefore kindly ask that no agencies or intermediaries submit offers or approaches.

Maastricht University is committed to promoting and nurturing a diverse and inclusive community. We believe that diversity in our staff and student population contributes to the quality of research and education at UM, and strive to enable this through inclusive policies and innovative projects led by teams of staff and students. We encourage you to apply for this position.