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Senior Research Associate - High-Dimensional Temporal Disaggregation in Official Statistics

Mathematics & Statistics
Salary:   £34,804 to £40,322
Closing Date:   Monday 15 February 2021
Interview Date:   To be confirmed
Reference:  A3254

Applications are invited for a Post-Doctoral Research Associate to develop high-dimensional methods for temporal-disaggregation in official statistics. This is an exciting opportunity to develop cutting-edge and theoretically-supported statistical methods that can combine multi-resolution data sources to provide an unrivalled picture of the national economy.

This research is funded by an ESRC Methodology Development Grant in close collaboration with stakeholders at the Office for National statistics, and with Dr Anders Kock at the University of Oxford.  Depending on your interests as a researcher, there is room to develop both theoretical and/or applied directions.   

You should have, or be close to completing, a PhD in Statistics, Operations Research, Econometrics, Signal Processing, Machine Learning, or a related discipline. You will work in a small team alongside the primary-investigator and their PhD student to undertake the research necessary to achieve the aims within the grant. This will include, for example, publishing in leading statistics, econometrics, or machine learning journals, presentation of research at workshops and conferences, developing code to implement new statistical methods, and active involvement in project meetings.  You will be experienced in one or more of the following areas: time-series analysis, econometrics, high-dimensional statistics, model-selection, optimisation. You will have demonstrated the ability to develop new statistical methodology and produce academic writing of publishable quality.

This position is available for start on the 1st of March 2021.

You will join us on an indefinite contract however, the role remains contingent on external funding which, at this time is for full time for 15 months in the first instance.

For this role we are open to discussing the possibility of reduced hours, job share, remote working, flexible start and finish times, or compressed hours.

Interested candidates are strongly advised to contact Dr Alex Gibberd (a.gibberd@lancaster.ac.uk) in advance of making an application.

Applications from people in all diversity groups are strongly encouraged.

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