PhD Candidate in Machine Learning for Official Statistics University of Luxembourg in Luxembourg

The University of Luxembourg is offering a PhD position in Machine Learning for Official Statistics, based at the Interdisciplinary Centre for Security, Reliability and Trust (SnT). The successful candidate will join the Security, Reasoning and Validation (Serval) research group and work on a partnership project with STATEC, Luxembourg’s National Statistics Institute.

The research will focus on the application of large language models (LLMs) to data-to-text problems, such as generating statistical reports and enabling semantic search across historical statistics publications. The project involves adapting LLMs and other machine learning models to tasks that mix text and structured data, evaluating their feasibility, practicality, and effectiveness through prototype implementations. The PhD thesis is part of a broader initiative at Serval and SnT to support the reliable deployment of machine learning systems in industry, providing practical tools for industry actors.

The position offers opportunities to collaborate with a diverse, international team and to disseminate research findings at conferences and in peer-reviewed publications. Applicants should have a Master’s degree in Computer Science, with a major in machine learning or software engineering considered an asset.

Strong programming and analytical skills are required, and industry experience in ICT is advantageous. Candidates should be committed, motivated, and able to work both independently and as part of a team. Knowledge of both foundational and recent developments in machine learning, especially large language models and generative AI, is expected. Fluent English communication skills are required; French is a plus.

The position is full-time (40 hours/week) for a fixed term of 36 months, located at the Kirchberg Campus. The annual gross salary is EUR 41,976. The University of Luxembourg values diversity and inclusion, and is committed to gender parity.

Applications should include a CV, cover letter, transcripts, and a research statement, and must be submitted online through the university’s HR system. Early applications are encouraged as review is ongoing.