Posted by Tony Whiteing, ITS University of Leeds on behalf of Prof. T X Mei, Salford University




POST-DOCTORAL RESEARCH ASSOCIATE
in Fault Tolerant Control of Railway Operations and Train Scheduling

SCHOOL OF COMPUTING, SCIENCE AND ENGINEERING, SALFORD UNIVERSITY

The Rail Research Group within the Engineering 2050 Research Centre of University of Salford is recruiting a Post-Doctoral Research Fellow (24 months) to work on an EPSRC funded project 'Challenging Established Rules for Train Control Through a Fault Tolerance Approach' in collaboration with the Institute of Transport Studies at The University of Leeds.
The project is one of six grants awarded from the EPSRC Strategic Partnership with railway industry, all concerned with "overcoming the constraints caused by stations and junctions on the railway network."
The Engineering 2050 Research Centre at the University of Salford consists of over 50 members of active researches including academics, research fellows and research students. The Rail Research Group within the research centre consists of a multi-disciplinary team and carries out world leading research for a wide range of rail applications, including train operations and optimisation, vehicle dynamics and control, traction control, condition monitoring, noise and vibrations, and rail bridge structures and stress analysis. Much of our research is externally supported by major funding bodies such as EPSRC, EU and TSB. We also have close links with industry which directly funds some of our work.
We are now seeking a highly motivated post-doctoral research fellow and are particularly interested in someone who wishes to become a R&D specialist in rail research and/or advanced control applications, pursuing a long term career in academia or industry.
The Research Project
The operation of a rail network is safe-guarded through the use of train control and protection systems which has to follow strict and often over conservative rules. However the use of such rules e.g. conservative speed profiles, on approaching signal blocks has a knock-on effect on other trains and can cause a network to operate at considerably less than its full achievable capacity.
This research project will develop a fault tolerant approach to the design and operation of the rail network, by integrating track design (e.g. the track layout, the positions of points and signalling blocks) with dynamic routing/scheduling, optimised using novel evolutionary computational approaches particularly suited for combining multi objective optimisation with safety/risk management. Enhanced fault tolerant capability would provide safety assurance so that, in normal operating conditions, trains can adopt much faster speed profiles when approaching a 'to-be-cleared' signal block at stations and junctions than those currently permitted. In the rare event of a 'fault' in the system, the train would be re-routed to take an alternative path. Increased capacity will be achieved through improved capability to handle disturbances and/or reduced operating constraints, without compromising the overall integrity of the system.
As the main aim of the project is to generate and demonstrate new railway network concepts for train operations and control, the scope of the research is multidisciplinary in nature including identification of suitable track sections/scenarios for case study, comprehensive understanding of current operational requirements and constraints and their effect on the case study sections, development of redundant train scheduling approaches, optimisation of the performance through the application of intelligent optimisation methods, and benefit quantification in terms of capacity and reliability compared with current practices.

PLEASE NOTE THAT THE DEADLINE FOR APPLICATIONS FOR THIS POSITION HAS BEEN EXTENDED TO 5 JUNE.
Details can be found at
http://www.jobs.ac.uk/job/ACR071/post-doctoral-research-assistant








Tony Whiteing



Dr  A E Whiteing
Institute for Transport Studies
University of Leeds
Leeds
LS2  9JT

Tel: 0113 343 5359
Fax: 0113 343 5334
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