2 PhD Positions in Small Area Estimation and Multisource Statistics for SDG Monitoring Utrecht University in Netherlands

Utrecht University’s Department of Methodology and Statistics is offering two PhD positions as part of a prestigious ERC Starting Grant project, “Small Area Estimation Methods to Monitor the Progress towards the Sustainable Development Goals at the Subnational Level in the European Union (SAESDGs-EU)”. The project aims to develop innovative small area estimation methods for producing high-quality composite SDG indicators, leveraging survey, census, and non-traditional data sources such as web and satellite data. This research will have real societal impact, shaping how economic well-being is measured across Europe at the local level.

The PhD positions focus on two distinct areas: (1) small area estimation in the presence of latent variables, and (2) small area estimation in the context of multisource data (probability and nonprobability data). The first position will contribute to computing composite indicators of multidimensional social indicators within the small area estimation framework, while the second will address the use of non-traditional data sources as auxiliary variables and multisource statistics issues.

Supervision will be provided by Dr Angelo Moretti, Dr Camilla Salvatore, and Prof. Peter Lugtig, who are part of the world-leading Data Quality group within the department. The group is renowned for its cutting-edge research in survey design, measurement error, nonresponse, data integration, register-based statistics, mixed-mode data collection, and methodological innovations for official statistics and social science research. Strong collaborations with national statistical institutes, international organisations, and academic partners ensure that research outcomes have both scientific and societal relevance.

PhD candidates will join a vibrant and collaborative research environment, with regular seminars, international visitors, and active participation in research networks. The group recently hosted ESRA25, the largest international conference in survey methodology, further highlighting its global reputation. Candidates will have opportunities to engage with leading scholars, present at international conferences, and contribute to high-impact publications.

Responsibilities include conducting high-quality methodological research in small area estimation, writing academic articles, blog posts, and statistical software, and disseminating research at conferences and relevant events.

Eligibility: Applicants must have a background in Statistics, Mathematical Statistics, Mathematics, or Social Statistics, or advanced methodological statistics training in the social sciences. Advanced programming skills in R, Python, or MATLAB are required, along with solid knowledge of statistical inference and modelling. Proficiency in English and academic writing skills are essential. Teamwork and autonomy are expected. Desirable qualifications include knowledge of survey sampling, structural equation modelling (for the latent variables position), data integration techniques (for the multisource data position), proficiency with GitHub, and demonstrated research skills.

Funding: The positions are fully funded by an ERC Starting Grant, offering a four-year contract with a gross monthly salary between €3,059 and €3,881 (full-time), 8% holiday pay, 8.3% year-end bonus, pension scheme, partially paid parental leave, flexible employment terms under CAO NU, and additional professional development opportunities.

Application: The deadline is 26 April 2026. Submit your CV and motivation letter via the Utrecht University website, specifying which position you are applying for. Interviews will be held in May and June, with a preferred start date of 1 September 2026.

Utrecht University is committed to diversity and inclusion, offering a supportive environment for all staff. The Faculty of Social and Behavioural Sciences is one of Europe’s leading faculties, with a strong focus on interdisciplinary research and teaching.