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Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS) Programme, Lancaster University – Senior Research Associate in Detecting Anomalous Structure in Streaming Data Settings (DASS)
Company Information: This £4M programme is funded by EPSRC and brings 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.
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).
Position Title: Senior Research Associate in Detecting Anomalous Structure in Streaming Data Settings (DASS)
Duties and Responsibilities: DASS will consider the foundational statistical challenges of identifying anomalous structure in data streams within constrained environments, handling the contemporary data realities, and identifying and tracking dependence across streams.
Position Qualifications: To apply 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 trade-offs; scalable statistical methods. You will also have shown a demonstrable ability to produce academic writing of the highest publishable quality and/or statistical computing skills.
Salary Range: £39,105 to £45,163
Benefits:
Website: https://www.anomaly-programme.org/
Application Information: For more information and where to apply, please look here; the closing date for applications for all posts is Wednesday 21st May, 2025.
Candidates who are considering making an application are strongly encouraged to contact Idris Eckley (i.eckley@lancaster.ac.uk) and Paul Fearnhead (p.fearnhead@lancaster.ac.uk).
Contact Email: dass@lancaster.ac.uk
Application Deadline: 05/21/2025
Company NameStatistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS) Programme, Lancaster University
Company Information
This £4M programme is funded by EPSRC and brings 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.
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).
Position TitleSenior Research Associate in Detecting Anomalous Structure in Streaming Data Settings (DASS)
Duties and Responsibilities
DASS will consider the foundational statistical challenges of identifying anomalous structure in data streams within constrained environments, handling the contemporary data realities, and identifying and tracking dependence across streams.
Position Qualifications
To apply 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 trade-offs; scalable statistical methods. You will also have shown a demonstrable ability to produce academic writing of the highest publishable quality and/or statistical computing skills.
Salary Range£39,105 to £45,163
Position or Company Websiteanomaly-programme.org
Application Instructions
For more information and where to apply, please look here; the closing date for applications for all posts is Wednesday 21st May, 2025.
Candidates who are considering making an application are strongly encouraged to contact Idris Eckley (i.eckley@lancaster.ac.uk) and Paul Fearnhead (p.fearnhead@lancaster.ac.uk).