PhD Research Fellow in statistics with focus on anomaly detection (ref 290920)

University of Oslo; Norway; On-site; NOK 450,000 - 550,000; Full time

Job summary

A prestigious university in Norway is seeking a PhD candidate to contribute to cutting-edge research in online anomaly detection within mathematical statistics. The successful candidate will engage in rigorous methodologies applicable to real-time streaming data, benefiting from interdisciplinary collaboration with the Statistics and Data Science group and external partners. This role is a full-time temporary position, and ideal candidates will have a strong theoretical background as well as an interest in computational efficiency.

Qualifications

• Applicants must have a strong background in mathematical statistics.
• Must master theoretical work and have an interest in computational efficiency.

Responsibilities

• Contribute to research in online anomaly detection.
• Work on rigorous, trustworthy, and transparent methods for streaming data.

Skills

Background in mathematical statistics

Interest in computational efficiency

Education

PhD in a relevant field

Job description

Organisation/Company University of Oslo Research Field Mathematics » Statistics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 9 Jan 2026 - 23:00 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

We welcome applicants with a strong background in mathematical statistics, who are eager to contribute to cutting-edge research in the field of online (real-time) anomaly detection in the setting of sequential data.The SODA project aims to produce rigorous, trustworthy and transparent methods with strong theoretical performance guarantees, addressing several pressing challenges in contemporary streaming data scenarios, such as evolving baselines and complex, data-intensive settings. The applicant must master theoretical work as well as having a strong interest in computational efficiency, which is fundamental in real time processing of streaming data.

The position is affiliated with both Integreat and the Statistics and Data Science research group at the Department of Mathematics. Integreat collects scientists from statistics and computer science and offers a flourishing machine learning community. The Statistics and Data Science group is active in a wide range of theoretical and applied areas of statistics and machine learning. The successful candidate will benefit from close collaboration across disciplines and access to diverse application areas through the joint environment of Integreat, the Statistics and Data Science group, and SODA partner the Norwegian Computing Centre. The SODA project also has close collaborations with Lancaster University.