About the Project
The project will investigate patterns of usage of charging stations and EV drivers (users) in a dataset of public EV charging in the Netherlands.
Problems to be considered include extracting governing equations from data to predict the spatial and temporal behaviour of users and the usage of charging stations. The studentship involves bringing together and extending methods from statistical learning and convex optimisation to develop rigorous and efficient tools for the automatic analysis of EV charging at various geographical and temporal scales.
A movie introducing the topic can be found
The successful applicant will be working within the Durham Institute for Data Science and the Durham Energy Institute, in close collaboration
with the Joint Research Centre Directorate C: Energy, Transport and Climate (European Commission) and our industrial partner ElaadNL.
Prospective candidates will be judged according to how well they meet the following criteria:
· First class honours (or high 2.1) degree in the mathematical sciences, computer science, physics, engineering or a related field.
· Strong understanding of statistics.
· Ability to undertake scientific programming in R and Python.
· Excellent written and spoken communication skills in English.
In the first instance, interested candidates are encouraged to make an informal enquiry to Dr Rui Carvalho email@example.com .
To apply formally for this studentship you need to submit a cover letter highlighting what you can bring to the project, CV and the names of two academic referees. Applicants will be required to submit a formal application using the online system https://www.dur.ac.uk/postgraduate/study/apply/
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.
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