Research Fellow in Data Analysis and Impact Assessment of Connected Transport
Are you an ambitious researcher looking for your next challenge?Do you have a background in big data analytics and evidence-based assessment of driver behaviour and acceptance of connected, cooperative and automated transport? Do you want to further your career in one of the UK’s leading research intensive Universities?
ITS has been awarded the three-year PAsCAL project from H2020 which aims to create the Guide2Autonomy, a novel framework that will improve the understanding of the implications of Connected and Autonomous Vehicles on society as well as educate their future drivers, passengers and those who will have to share the road with them. PAsCAL will make use of a strongly interdisciplinary mix of tools from both human and technological sciences (e.g. big data analytics, deep learning), to capture the public’s acceptance and attitude, analyse and assess their concerns, identify major obstacles/barriers that may hinder the social acceptance of CAVs, model and simulate realistic scenarios for the creation of best practices, and validate its findings in a number of real-world trials.
You will work with and in support of Dr Chen’s research to ensure project tasks are successfully completed. Primarily you will be working on the PAsCAL project and additionally will support other projects as and when required (and thus the contract may be extendable where appropriate).
To explore the post further or for any queries you may have, please contact:
Dr Haibo Chen, Principal Research Fellow
Tel: +44(0)113 343 5355, email: H.Chen@its.leeds.ac.uk
Location: Leeds - Main Campus
Faculty/Service: Faculty of Environment
School/Institute: Institute for Transport Studies
Grade: Grade 7
Salary: £33,199 to £39,609 p.a.
Working Time: 100% - We will consider job share/flexible working arrangements
Post Type: Full Time
Contract Type: Fixed Term (until 31 May 2022 due to external funding)
Closing Date: Friday 22 November 2019
Interview Date: To be confirmed
Downloads: Candidate Brief