| Location: | Lancaster |
|---|---|
| Salary: | £39,906 to £46,049 |
| Hours: | Full Time |
| Contract Type: | Permanent |
| Placed On: | 24th April 2026 |
|---|---|
| Closes: | 30th May 2026 |
| Job Ref: | 0264-26 |
Location: Bailrigg, Lancaster, UK
(Full-Time/Indefinite with End Date)
Interview date: Wednesday 17th June 2026
We invite applications for a Post-Doctoral Research Associate position to join the Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS) Programme, based at Lancaster University. The DASS Programme will consider the foundational statistical challenges of identifying anomalous structure in streams within constrained environments, handling the realities of contemporary data streams, and identifying and tracking dependence across streams.
This £4M programme is funded by EPSRC and brings together research groups from the Universities of Lancaster, Bristol, Warwick and the London School of Economics together with a committed group of industrial and public sector partners.
Interaction between the research groups at the universities will be strongly encouraged and resourced; our philosophy is to tackle the methodological, theoretical and computational aspects of these statistical problems together. This integrated approach is essential to achieving the substantive fundamental advances in statistics envisaged, and to ensuring that our new methods are sufficiently robust and efficient to be widely adopted by academics, industry and society more generally.
The programme is led by Idris Eckley (Lancaster University), Haeran Cho (University of Bristol), Paul Fearnhead (Lancaster University), Qiwei Yao (London School of Economics) and Yi Yu (University of Warwick).
This 2-year position is available at Lancaster University. You should have, or be close to completing, a PhD in Statistics or a closely related discipline. Throughout, you should have demonstrated an ability to develop new statistical theory and methods in one of the relevant areas, including but not limited to: anomaly detection; changepoint analysis; non-stationary time series analysis, high dimensional statistics, statistical-computational tradeoffs, scalable statistical methods. You will also have shown a demonstrable ability to produce academic writing of the highest publishable quality.
This is a are full-time position, though we will consider applicants requesting part-time or other flexible working arrangements.
Candidates who are considering making an application are strongly encouraged to contact Idris Eckley (i.eckley@lancaster.ac.uk) or Paul Fearnhead (p.fearnhead@lancaster.ac.uk) to discuss the programme in greater detail.
Please note: unless specified otherwise in the advert, all advertised roles are UK based.
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