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Vision Based Robotic Guidance and Monitoring Using 3D Sensor and Industrial Internet of Things

Vision Based Robotic Guidance and Monitoring Using 3D Sensor and Industrial Internet of Things

Eligibility: UK/ EU graduates with the required entry requirements

Reason for eligibility restriction: Funder requirement

PhD funding award: Bursary plus tuition fees (UK/ EU)

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 30 June 2019

Interview dates: Will be confirmed to shortlisted candidates

Start date: September 2019

Enquiries may be addressed to Dr. Hafiz Ahmed with cc to Dr. Dina Laila.

The project

Oxford Vision and Sensor technology (OVST) designs and develops vision system for industrial application, especially for the automotive industry. The current sensor uses stereoscopic vision for 3D reconstruction. The literature reveals that the current research focuses predominantly on using stereoscopic vision that can measure the depth information only. However, from an industrial point of view, full 3D models of the components under dynamic environmental conditions (e.g. varying lighting, uneven surface) are preferable to decrease the defective production rate. This PhD project aims to narrow the research gap by developing innovative sensor system, sensor fusion and filtering algorithms for OVST’s vision system. Development of embedded signal and image processing platform for computer vision and cloud-based data processing and visualisation are other aims of this PhD project. The results will contribute towards making OVST’s vision system ever competitive in the context of Industry 4.0 and IIOT.

The project will be based in the Control, Sensing and Learning laboraotory at Coventry. This laboratory is part of the Institute of Future Transport and Cities (FTC) and is dedicated to the application of control, sensing and learning technologies for various industrial applications.

Funding details

The scholarship will pay an annual stipend at the standard UKRI rate and covers 100% tuition fees at the UK/EU rate for 3.5 years.


The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

Entry requirements

  • Successful candidates will have at least a minimum 2:1 first degree in Computer Science, Engineering, Mathematics/Statistics, or a related discipline (and preferably a Masters degree)
  • A minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)
  • Real passion for computer vision, and would like to be involved in building cutting edge technology in the fields of Machine Vision, Robotics and the Industrial Internet of Things (IIoT).

Desired skills:

Software Programming:

Experience of programming in C#, C++ or similar languages. The candidate should be able to understand software and hardware integration requirements.

Machine Vision:

Experience in Image Processing, Computer Vision and Optics Design would be an advantage. A familiarity with image processing applications such as Halcon HDevelop or Cognex VisonPro would be of particular interest.

A knowledge of more general mathematics packages such as Matlab or OpenCV would also be useful.


Experience in Robotics and the Industrial Internet of Things (IIoT) would also be advantageous. An ability to read a circuit schematic and some simple soldering skills would be appreciated.


Excellent communication skills and an ability to interact professionally and productively with other team members. The candidate should be able to present well, plan, be confident and proactive.

How to apply

Enquiries may be addressed to Dr. Hafiz Ahmed with cc to Dr. Dina Laila.

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.

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