Dear all,

Geocomputation Research Domain at the King's College London is inviting applications for a fully funded PhD in 'Improving Efficiency and Equity of Ambulance Services through Advanced Demand Modelling'. Please find details below. 

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Job posting – Full-funded PhD position (LISS-DTP 1+3/3+)
 
Overview of the position
We are looking for a PhD student who will join the Geocomputation Research Domain at the Geography Department of King’s College London. The appointed PhD student will work on the project titled “Improving Efficiency and Equity of Ambulance Services through Advanced Demand Modelling”, funded by the ESRC – LISS-DTP and supervised by Dr Chen Zhong and Prof. Judith Green. This PhD project is a collaborative project together with London Ambulance Service (LAS), where Dr Leanne Smith will supervise the project as an industrial advisor. We are seeking a candidate for 1+3/3+ term depending on the qualification of the candidate. For more information about the scholarship, please visit LISS-DTP (https://liss-dtp.ac.uk/case-studentships-student-applicants/). 

King's Geocomputation research (https://kingsgeocomputation.org/) spans a range of contemporary social, environmental and geographical issues, collaborating intensively with experts in the field from universities abroad. PhD candidate selected for this project will also have the opportunity to closely work with the forecasting and planning team at LAS, and research domain/centres at KCL including CUSP, SUPHI.

Project non-technical summary
Demand for Ambulance Services in England has risen dramatically over recent years, with growing pressure anticipated for future years. The disparity between the increasing demand and limited ambulance resources makes the major challenge for maintaining a high-quality service. In 2017, NHS England undertook a significant national reform called the Ambulance Response Programme (ARP), designed to address efficiency and performance issues. It noted the over-use of immediate dispatch decisions and the insufficient allocation of resources to incidents. Key issues concerned: the quality of care; its cost-effectiveness, and the equality of provision across areas and population groups. In view of the growing pressures of NHS, and the necessity of ambulance services to understand the needs of the populations they serve, the proposed PhD project aims to develop an advanced demand prediction model for ambulance services taking LAS as a case study. The research is to find the best correlated socioeconomic, environmental, and spatiotemporal factors and to model these factors as predictors of ambulance demand. The final component of the PhD will develop the implications of the model as Demand Management innovations, for future testing.

Required qualifications
- You must be eligible for ESRC funding and will be asked to pursue either the 1+3 (MSc+PhD) or +3 (PhD only) track.  The cost of the MSc is included in the ESRC award.
- MSc/BSc degree in Urban Informatics, Urban Analytics, Spatial Analysis, Transport planning, Geography, or related field 
- Excellent written and oral communication in English
- Knowledge of spatial data analysis techniques and GIS applications are an asset

Application instructions 
Applications for admission to the 2019/20 Masters programme close in March 2019. To ensure anyone on 1+3 track to be admitted to the relevant Master's programme in time, we have set an application deadline of 23:59 on Thursday 21ST Feb 2019. All studentship applications must include:

1. A CV (no more than 2 pages) highlighting relevant study and work experience. 
2. A cover letter (no more than 2 pages).
3. 2 references, at least one of which will be academic.
4. copies of transcripts for all relevant degrees

We expect to conduct in-person interviews in late February or the first week of March. Please contact Dr Chen Zhong ([log in to unmask]) for any project related questions. You should email LISS DTP ([log in to unmask]) if you have any general questions regarding the application process, or core methods training requirements. 

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