Publisher
source

Umeå University

PhD in Statistics: Latent Variable Models for Social Data Science Umeå University in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

The PhD position is fully funded for four years. PhD students may be appointed to teaching and/or consulting duties up to 20% of their time, which may extend the program duration to ensure four years of graduate studies. Monthly salary is mentioned, but no amount is specified.

Deadline

Nov 4, 2026

Country flag

Country

Sweden

University

Umeå University

Social connections

How do I apply for this?

Sign in for free to reveal details, requirements, and source links.

Apply for this position

Keywords

Computer Science
Sociology
Psychology
Mathematics
Probability Theory
Computational Social Science
Statistics
Statistical Modelling
ML

Suggested positions

About this position

Umeå University (Sweden) is recruiting one to two PhD students in Statistics for the research project “Next-Generation Latent Variable Models for Social Data Science” at the Umeå School of Business, Economics and Statistics (USBE), Department of Statistics.

The project focuses on statistical theory, methodology, and computational methods for complex social science data, with emphasis on latent variable models and their connections to modern machine learning. The research is motivated by applications where important constructs such as ability, attitudes, well-being, and dimensions of poverty are not directly observable and must be inferred from data. The successful candidates will also have opportunities to collaborate with external partners in digital learning and the charity sector.

This is a fully funded PhD position for four years. Students may be assigned teaching and/or consulting duties up to 20% of their time, which may extend the appointment to preserve four years of doctoral study. The starting date is January 1, 2027, or by agreement. The application deadline is 2026-11-04.

Eligibility highlights: an advanced-level degree with a major in Statistics (or equivalent), plus mathematics coursework equivalent to at least 30 ECTS credits. The department also seeks a strong quantitative background, very good programming skills, demonstrated interest in statistical modelling, and strong motivation to develop methodology for societally relevant questions. Experience in statistical machine learning is a particularly strong merit.

How to apply: submit a motivation letter, CV, the “New PhD” form, copies of undergraduate and graduate theses, degree certificates and other supporting documents, and contact details for at least two referees through the university recruitment system.

Supervision/contact information listed in the post includes PhD Gabriel Wallin and Professor Marie Eriksson at Umeå University.

Funding details

The PhD position is fully funded for four years. PhD students may be appointed to teaching and/or consulting duties up to 20% of their time, which may extend the program duration to ensure four years of graduate studies. Monthly salary is mentioned, but no amount is specified.

What's required

Applicants should have an advanced-level degree with a major in Statistics or equivalent qualifications. They must have completed Mathematics courses equivalent to at least 30 ECTS credits. Strong quantitative background in statistics, mathematics, computer science, or a related field is expected, along with very good programming skills, demonstrated interest in statistical modelling, and strong motivation to develop statistical methodology for societally relevant questions. Strong merit is demonstrated theoretical and applied knowledge in statistical machine learning, including implementation.

How to apply

Prepare a letter of application describing your research interests and relevance to the position, a CV, the New PhD form, copies of undergraduate and graduate theses, degree certificates and supporting documents, and contact details for at least two referees. Submit the application through the university recruitment system by 2026-11-04.

More information can be found here

Ask ApplyKite AI

Start chatting
Can you summarize this position?
What qualifications are required for this position?
How should I prepare my application?