PhD Student Position at Lancaster University, U.K.

The Department of Mathematics and Statistics at Lancaster University in offering a fully funded, 3-year PhD position in Statistics or Social Statistics. The position is funded through a CASE studentship at ESRC’s North West Social Science Doctoral Training Partnership and it is in collaboration with an energy data services start-up company, DESCO Analytics.

Project title: Characterising the influence of stakeholders on building energy consumption

Project overview: This project aims to develop advanced quantitative methods to identify and quantify the relationships of a range of stakeholder groups on the energy consumption of a building. These stakeholder groups include building occupants, maintenance providers, facility managers, designers and financial decision makers. For this project, DESCO Analytics (DA) – in the process of creating an energy analytics and intelligence suite for Lancaster University (LU) – are providing real-world data from a living and working campus for more than 15,000 people each day, comprised of over a 100 buildings.

While energy data analysis is not new, gaining actionable insight using conventional methods and software suites poses several challenges. Not only is modern data too large in size, it is also fairly complex, and subject to noise. The analysis and interpretation of the results are highly non-standard and span multiple domains. Therefore, in addressing these challenges, the candidate will investigate the use of High-Dimensional Statistics and Functional Data Analysis (FDA) approaches as advanced statistical modelling techniques.

Bandidate background: We are seeking a strong and motivated candidate with a Master degree in Mathematics, Statistics, Data Science or related field. Experience in data science related coding skills using python, R, Stata, Matlab or C is essential. You are able to work in an international team, able to work independently and flexible to manage the more independent schedule of academia and the fast-paced environement of a start-up. Some knowledge of envergy vectors and process management is preferred, and experience in working with incomplete, irregular, real-world time sereis datasets is beneficial. Working experience with big data platform (Hadoop, Hortonworks, Cloudera) and in a Linux environment (Apache, Docker) is beneficial.

Supervisors: The candidate will be jointly supervised by Dr. Juhyun Park (Mathematics and Statistics) and Dr. Denes Csala (DA data scientist). The supervising team will bring extensive experience in nonparametric regression and smoothing methods, time series data analysis and longitudinal and functional data analysis, energy analytics, complex sociotechnical systems, machine learning and data visualization.

Starting date: Jan 2019

How to apply: In the first instance, interested candidates are advised to send their CV and transcripts to Dr. Juhyun Park ( for advice and further information. Formal application (CV, transcripts, personal statement up to two pages, two references) needs to be submitted online for PhD Statistics with entry Jan 2019
In the ‘Personal Statement’, please write one page of your suitability for this research project, headed ‘Characterising the influence of stakeholders on building energy consumption’.

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