Senior Research Associates in Detecting Anomalous Structure in Streaming Data Settings (DASS) x4
Lancaster University, Bristol
Senior Research Associates in Detecting Anomalous Structure in Streaming Data Settings (DASS) x4
£45163
Lancaster University, Bristol
- Full time
- Permanent
- Onsite working
Posted 5 days ago, 15 Dec | Get your application in now to be included in the first week's applications.
Closing date: Closing date not specified
job Ref: 923b18aa23c2433b93c2647d3a8ecaee
Full Job Description
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).
These 2 year positions are available at each of the partner universities. 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.
Find out what it's 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 their 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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