Reference : A3249-S
We invite applications for a Senior Research Associate position to work on the development of novel statistical methods within the recently awarded, £1.
6M EPSRC Reducing end use Energy Demand in Commercial Settings Through Digital Innovation programme, together with a number of major UK industrial partners.
This project is an exciting cross-disciplinary collaboration between researchers within the departments of Mathematic & Statistics, Computer Science and Environment Sciences at Lancaster University.
The programme’s goal is to create new tools for helping organisations respond to climate change and net zero challenges.
Through building an understanding of these patterns the project aims to identify and recommend energy saving strategies for industrial users.
The project is led by Idris Eckley (Mathematics & Statistics), and Adrian Friday (Computing), together with Alex Gibberd (Mathematics & Statistics) and Ally Gormally (Lancaster Environment Centre).
Lancaster’s internationally recognised Statistics group is one of the largest and strongest Statistics groups in the UK with 25 academic staff, a vibrant community of Post-doctoral researchers and research students.
The group sits within the Department of Mathematics and Statistics, one of the UK’s top departments in Mathematics and Statistics, ranked 5th overall in the 2014 Research Excellence Framework and 3rd for the impact of its research.
The department provides an environment which aims to meet the individual needs of each member of staff. We are committed to family-friendly and flexible working policies, and seek to promote a healthy work-life balance.
The University is a charter member of Athena SWAN and has held a Bronze award since 2008, in recognition of good employment practice to address gender equality in higher education and research.
The Department achieved its own Athena SWAN Bronze award in 2017 and is a registered supporter of the London Mathematical Society’s Good Practice Scheme.
This position will focus on the development of new time series and changepoint methods for, and applied to, multi-dimensional industrial energy and contextual data to identify potential for replicable savings in energy.
You should have a PhD in Statistics or a related discipline. You will be experienced in one of more of the following areas : anomaly detection, changepoint analysis, non-stationary time series analysis, high dimensional statistics, statistical-computational tradeoffs, signal processing or novel inference methods for streaming data.
Demonstrable ability to produce academic writing of the highest publishable quality is essential. Experience of developing research-level software is desirable but not essential.