Senior Research Associate in Detecting Anomalous Structure in Streaming Data Settings (DASS) - 1435-24-R

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Senior Research Associate in Detecting Anomalous Structure in Streaming Data Settings (DASS)

School of Mathematical Sciences
Location: Various
Salary: £39,105 to £45,163 (Full time, indefinite with end date)
Closing Date: Wednesday 21 May 2025
Interview Date: To be confirmed
Reference: 1435-24-R

We invite applications for Post-Doctoral Research Associate positions to join the Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS) Programme. 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.

This programme will be 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 methods or theory 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 full-time position, though we will consider applicants requesting part-time or other flexible working arrangements. We welcome applications from people in all diversity groups.

Candidates who are considering making an application are strongly encouraged to contact Idris Eckley ( i.eckleylancaster.ac.uk ) and Paul Fearnhead ( p.fearnheadlancaster.ac.uk ) to discuss the programme in greater detail.

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