Overview
Senior Research Associate - DASS, School of Mathematical Sciences. Location: Bailrigg, Lancaster, UK. Salary: £39,906 to £46,049 (Full-Time/Indefinite with End Date). Closing Date: Saturday 30 May 2026. Interview Date: Wednesday 17 June 2026. Reference: 0264-26.
About the Programme
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).
Role and Qualifications
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.
Working Arrangements
This is a full-time position, though we will consider applicants requesting part-time or other flexible working arrangements.
How to Discuss the Programme
Candidates who are considering making an application are encouraged to contact the programme leads to discuss the programme in greater detail.
Practical and Equality Information
UK based requirements apply unless specified otherwise in the advert. Find out what it is like to work at Lancaster University, including information on our wide range of employee benefits, support networks and our policies and facilities for a family-friendly workplace. The University recognises and celebrates good employment practice undertaken to address all inequality in higher education whilst promoting the importance and wellbeing for all our colleagues. We warmly welcome applicants from all sections of the community regardless of age, religion, gender identity or expression, race, disability or sexual orientation, and are committed to promoting diversity, and equality of opportunity.
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