Wanted – exceptional doctoral candidates to undertake trailblazing, transformative research alongside outstanding early-career researchers.

Coventry University (CU) is inviting applications from suitably-qualified graduates for a fully-funded PhD studentship

This doctoral (PhD) project has been devised and developed by a leading early-career researcher at Coventry University. The Trailblazer Scheme provides doctoral researchers with an innovative and dynamic intellectual space in which to undertake transformative research, whilst fully supported by a team of experienced supervisors

Coventry University has been voted ‘Modern University of the Year’ for three straight years (The Times/Sunday Times Good University Guide 2014−2016) and is ranked in the UK’s top 15 overall for the fifth year in a row (Guardian University Guide). We have a global reputation for high quality teaching and research with impact. Almost two-thirds (61%) of our research was judged ‘world leading’ or ‘internationally excellent’ in the Research Excellence Framework (REF) 2014.

Covering the full spectrum of land, rail, air and water-based transport, the Institute for Future Transport and Cities (IFTC) at Coventry University addresses the whole innovation chain from design, materials, advanced manufacturing, systems and supply chain as well as the business environment. Within the IFTC, the Autonomous Vehicles & Artificial Intelligence Laboratory (AVAILab) has been recently established. While open to a wide spectrum of applications, its main motivation is in conducting research that involves mathematical modelling, optimisation, soft and natural computing, self-organisation, swarm robotics and autonomous navigation.

Coventry University is inviting applications from suitably qualified graduates for a fully-funded PhD studentship. The project will be carried out within the AVAILab (https://availab.org/) under the supervision of Dr Mauro S. Innocente (https://msinnocente.com/, https://pureportal.coventry.ac.uk/en/persons/mauro-innocente).

PhD project

Predictive maintenance in the railways and roads is paramount to maintain these critical systems in continuous operation. Maintenance issues include missing fasteners, deformed track geometry, subgrade/ballast instabilities, structural health problems, failures, obstructions, potholes, roadside vegetation, damaged guardrails, etc. Inspections typically involve foot-patrols, trolleys and/or measuring vehicles. These can be accurate at the expense of significant manpower and time. Maintenance is often reactive (too late) or preventive, regularly performed to decrease the probability of failure (too early). Instead, Predictive Maintenance (PdM) is informed by the infrastructure condition rather than its expected lifespan. PdM has been attempted using pattern recognition systems based on test-vehicle data. However, this requires the interruption of the normal traffic.

The use of remotely-controlled monitoring drones to identify maintenance needs has been proposed, with preliminary trials showing that data-acquisition time is drastically reduced. Drone-based inspection does not require stopping the traffic and can work in areas inaccessible to human operators. However, the use of Artificial Intelligence (AI) enabled autonomous drones is yet to be explored.

This project will investigate the railways and roads maintenance current practices and technologies, as well as relevant state-of-the-art autonomous navigation and pattern recognition algorithms. The aim is to identify application-based improvements and to develop a drone-based autonomous intelligent system to detect maintenance needs without disrupting the normal traffic of these transport systems.

Benefits

  • Our research strategy is underpinned by a £250m investment in research and facilities
  • Dedicated Doctoral College and Centre for Research Capability Development deliver high quality professional support for researchers, from PhD to Professor.
  • Free training: research career planning, managing your doctorate, research communication skills, research ethics, research impact, research integrity, research methods and research supervision.

Coventry is a member of the Doctoral Training Alliance (DTA), the largest multi-partner and only nationwide doctoral training initiative of its kind.

Administrative Contacts & How To Apply

To find out more about the project, please contact:
Dr Mauro S. Innocente ([email protected]).

To apply online, please visit https://pgrplus.coventry.ac.uk/.

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

Entry Requirements

Entry criteria for applicants to PHD

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.

PLUS

  • The potential to engage in innovative research and to complete the PhD within 3.5 years.
  • A minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).

Additional candidate specification

ESSENTIAL

  • A first degree in Aerospace Engineering, Mechanical Engineering, Computer Science, Robotics, Artificial Intelligence (AI), or another relevant discipline.

DESIRABLE

  • A postgraduate degree in Aerospace Engineering, Mechanical Engineering, Computer Science, Robotics, AI, Autonomous Systems, Data Science, or another relevant discipline.
  • Experience of working with ROS.
  • Experience of working with Gazebo simulator.
  • Expertise in Pattern Recognition.
  • Expertise in Guidance, Navigation and Control.
  • Programming skills in Matlab, Python and/or C++.
  • Prior knowledge of road and/or railways maintenance.

For further details see:

https://www.coventry.ac.uk/research/research-students/making-an-application/

Funding Notes

This is a full studentship, which includes tuition fees and living expenses for a doctoral candidate over 3.5 years.

Stipend rates set by UKRI with an annual projected average increase of 1.25% per year. Stipend for the first year will be £15,009

Funding Information

Funding applies to:

UK/EU and International students

Application Deadline:

30 April 2020

Please see our website for how to apply:
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