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University Academic Fellow in Big Data and Transport Modelling

Traffic congestion costs £20bn per year to the UK economy. Active Traffic Management (ATM) has been designed to reduce traffic congestion through responsive control methods including variable speed limits, ramp metering on freeways or signal timings in urban networks. Traffic prediction models are a crucial part of such ATM programs that underpin traffic network reliability and efficiency and an area where the Institute for Transport Studies has a globally leading profile. In the past decade, network modelling has advanced rapidly with the development of dynamic traffic models that describe the dynamics of the traffic network in space and time. The advances in information and communication technologies (ICT) and the availability of real-time large scale traffic data, together with the increasing penetration of intelligent vehicles in traffic now make it possible to include real-time information in traffic models. Utilising such real-time traffic data available from multiple sources (i.e. Bluetooth, mobile phones, loop detectors or wifi) will make significant breakthroughs in traffic modelling theory and practice.

You will play a crucial role in maintaining our world leading academic profile whilst diversifying further the range of skills that can be brought to bear.  You will provide a step-change in our capacity to engage with this area.   As an all round academic, you will develop a research profile that will contribute to the University’s ambition to excel at REF2020, with a sustained record of internationally excellent (and some world-leading) publications and a strong record of presentations at international conferences.  You will be expected to collaborate with colleagues within the Institute and forge links with the Leeds Institute for Data Analytics and the departments of Maths and Computing in order to build research proposals in the area of Big Data/Smart Cities. You will be expected to access funding opportunities coming from national and international sources in the area of real-time traffic management and planning of large-scale networks and to engage with organisations on the real-world application of the research.

On the teaching side, we see the Transport Systems Catapult as a key source of innovation funding in the Sector; the Transport Systems Catapult is positioning the UK as a Centre of Excellence in transport modelling and we aim to provide the globally leading programme to support that (from 2016).  As such you will make an important contribution to our proposed new advanced modelling masters’ programme on how to use big data in modelling transport.

With a PhD in a relevant related discipline such as mathematics, computer science, engineering or transport studies, you will have a strong research record and experience within the field of Transport Modelling which makes use of large data sources, the ability to teach at Postgraduate level, as well as a clear and compelling vision for personal academic development.

As part of the application process you will be required to upload the following documents:
1.    A CV;
2.    A list of publications;
3.    A statement detailing your research and academic plan (no more than 2 sides of A4).

For informal enquiries about the role please contact Professor Simon Shepherd, tel: +44 (0)113 343 6616, email: S.P.Shepherd@its.leeds.ac.uk.  

For enquiries about the application process please contact the recruitment team, tel: +44 (0)113 343 0518, email: 250GreatMinds@leeds.ac.uk.

To find out more about Academic Fellowships and our 250 Great Minds recruitment campaign please visit: 250GreatMinds.leeds.ac.uk.

Please also see other opportunities in Data Analytics.

Location:  Leeds - Main Campus
Faculty/Service:  Faculty of Environment
School/Institute:  Institute for Transport Studies
Category:  Academic
Grade:  Grade 8
Salary:  £38,511 to £45,954
Post Type:  Full Time
Closing Date:  Sunday 16 November 2014
Reference:  ENVTR1003
Downloads:  Job Description  

The closing date for this job opportunity has now passed, and applications are no longer being accepted for this position

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