Biostatistician or Quantitative Researcher Machine Learning

Are you a biostatistician or quantitative researcher with proven expertise in machine learning applied to clinical or health data? The SYNTHESIS project, funded by an ERC Consolidator Grant, is seeking a biostatistician to provide advanced statistical and machine learning support at the core of a groundbreaking effort to personalise psychotherapy for depression. Candidates without demonstrated experience in applying machine learning methods will not be considered for this position.

The SYNTHESIS project

Depression affects hundreds of millions of people worldwide, yet more than half of patients do not respond to standard psychotherapy. The SYNTHESIS project, led by Prof. Dr. Eirini Karyotaki at the University of Amsterdam, addresses this challenge directly. By integrating Individual Patient Data (IPD) from over 573 randomised controlled trials, including more than 73,000 patients and combining advanced network meta-analyses with machine learning, SYNTHESIS aims to identify which psychotherapy works best for whom. As the project biostatistician, you will be the methodological engine behind the most innovative part of SYNTHESIS: the integration of machine learning into Individual Patient Data (Component) Network Meta-Analyses. Working closely with the postdoctoral researchers, PhD candidates, and Prof. Dr. Karyotaki, you will develop and implement the ensemble learning pipelines, predictive models, and interpretability frameworks that will ultimately inform personalised clinical decision-making tools for psychotherapy.

What are you going to do

• Provide guidance on developing machine learning pipelines within the IPD(c)NMA framework using ensemble methods;


• Support the development of risk prediction models and their integration into meta-analytic models;


• Advise on applying interpretability methods (e.g., Shapley values) for clinically meaningful insights;


• Guide validation strategies (internal and external cross-validation) to ensure robustness and generalizability;


• Support the team in open science practices, tool development, and scientific dissemination.

What do you have to offer

• A PhD in Biostatistics, Statistics, Data Science, Computer Science, Epidemiology, or a related quantitative discipline;


• Proven expertise in applying machine learning methods to clinical, healthcare, or large-scale data;


• Experience with advanced statistical modelling, predictive analytics, and validation techniques, preferably within clinical trials or meta-analytic research;


• Strong programming skills in R and/or Python, with experience in reproducible and open science workflows (e.g., GitHub, code sharing, documentation).

What else do we offer you

• A part-time biostatistician position (0.4 FTE) for a period of 12 months with a possible extension to 4 years.


• Salary in scale 10 accordance with the Dutch University Collective Labour Agreement (CAO)


• 8% holiday allowance and 8.3% end-of-year bonus.


• Excellent secondary benefits including generous leave entitlement, ABP pension, and full access to UvA facilities.


• A central methodological role in a high-impact, internationally visible research project.


• Close collaboration with Prof. Dr. Eirini Karyotaki and a team of researchers at the forefront of Precision Psychology.


• Access to a network of world-leading advisors in biostatistics, machine learning, and psychotherapy research.


• Budget for attending relevant conferences and training.


• Hybrid and flexible working arrangements are possible.

You will be working in this department

You will join the research group of Prof. Dr. Eirini Karyotaki within the Department of Psychology (Programme group: Clinical Psychology), Faculty of Social and Behavioural Sciences, University of Amsterdam. The full SYNTHESIS team consists of two PhD candidates, two postdoctoral researchers, and yourself as the project biostatistician. You will work most closely with the postdoctoral researchers and PhD candidates on the analytical work packages and will have access to an international advisory board with deep expertise in biostatistics, machine learning, and clinical psychology. The team fosters an open, collegial, and international culture grounded in open science principles, with regular team meetings and an international advisory board.

Please submit your application in one bundled English PDF file, including:

• A motivation letter describing your statistical and machine learning expertise and how your profile fits this position (max. 1 page);


• A full CV;


• Evidence of applied machine learning experience, such as a published paper, preprint, or a link to a code repository or portfolio;


• Contact details of two academic or professional referees.

The closing date for applications is 9 June 2026. Preselected candidates will be invited for an interview, which is expected to take place in the last week of June/ first week of July. The position starts on 1 September 2026. Apply via the UvA vacancy portal. For questions about the position, please contact Prof. Dr. Eirini Karyotaki at [email protected].