진학프로석·박사 채용 접수 플랫폼

Specific Requirements

To strengthen our research on the impacts of conflict, PRIO seeks a motivated researcher with a background in social sciences, or a strong interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other, related projects led by Hegre, such as the Violence and Impacts Early-Warning System (VIEWS).

Key responsibilities

• • Develop new forecasting models and methods that advance the methodological foundations of conflict prediction.
• • Lead the design and improvement of ensemble routines and validation protocols in the VIEWS forecasting system.
• • Publish research findings in top peer-reviewed journals and contribute to high-quality technical reports.
• • Collaborate with developers and social science researchers to ensure methodological innovations are integrated into operational forecasting pipelines.
• • Supervise and mentor junior researchers and contribute to building methodological capacity.
• • Engage with academic and policy communities to disseminate research and communicate methodological advances.

Benefits

PRIO offers

• • Contract: 1.5 years, full-time temporary employment, with a salary based on qualifications and experience.
• • Location: The institute is located on spacious and attractive premises in central Oslo. The work will be performed in Oslo, Norway (on-site presence required).
• • All employees are offered membership in the Norwegian Public Service Pension Fund.
• • Additional employee benefits include a subsidized canteen, a weekly running group and various social events. Moreover, PRIO provides an excellent work environment.
• • PRIO is an equal-opportunity employer and values staff diversity. For more information on our HR and recruitment approach, please consult our recruitment principles and careers page.

Eligibility criteria

Qualifications

Required

• • PhD in a relevant field (e.g. political science, statistics, computer science, informatics, economics, or related discipline) with a demonstrated focus on forecasting, statistical modelling, and/or machine learning.
• • Strong technical background, including substantial experience with programming in Python and relevant ML/statistics libraries (e.g. PyTorch, Darts, Hugging Face Transformers, Pyro, PyMC, GPyTorch).
• • Proven ability to carry out independent research, with a track record of peer-reviewed publications.
• • Experience working collaboratively in research teams, including the use of GitHub or other version control systems.
• • Excellent analytical, writing, and communication skills in English.

Desirable

• • Experience developing and evaluating models for time series, tempo-spatial, and/or probabilistic forecasting.
• • Familiarity with ensemble methods, Bayesian approaches, and uncertainty estimation.
• • Experience with large-scale or messy real-world data (structured and/or unstructured).
• • Interest in or experience with text-as-data methods, including modern LLM-based approaches.
• • Familiarity with collaborative projects involving both social scientists and software developers.
• • Experience or strong interest in working with large deployed and productionised machine learning pipelines.

Selection process

Application procedure

Submit the following documents in English and PDF format via PRIO’s online form by 15 January 2026:

• • Application letter (1–2 pages) addressing how you meet the required and desired qualifications. Please also describe your relevant experience and your motivation to apply for this position;
• • Up to 3 (three) academic writing samples (single PDF);
• • CV in English, including a list of publications and a link to your GitHub repository (if you have one);
• • Transcripts of academic degrees/certificates (single PDF);
• • Name and full contact information for 3 (three) references.

Note: When filling out the online form, candidates will be requested to provide the full name and contact details of three references. The references will be contacted after the interview stage.

Assessment procedure

Selected candidates will be invited for an interview (in person or online) with the hiring committee. Candidates’ references will only be contacted after the interview stage.