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Data Driven Information Recovery in Sensor Systems

  • Full or part time
  • Application Deadline
    Wednesday, November 13, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

This PhD project will enable to researcher to study transformative technologies for rethinking how we sample and record data from a wide variety of sensors. This PhD project will be part of a major research project to study “Signal Processing in the Information Age” and a major goal is to evaluate the fundamental limits of information recovery.

Most signal processing systems for sensors, e.g. radar, lidar or wireless receivers, dispose of much of the recorded information along the processing chain. This is usually desirable and necessary so that the sensor signal can be converted into useful information in an energy efficient manner. However, improvements in processor power and the availability of low cost, large scale memory means that it is now timely to rethink this paradigm. Some of this “lost” information may be very valuable and worth recovering. For example, when the sensor system must be rapidly reconfigured beyond its intended use to address an unforeseen and imminent problem or threat. Also, with the rapid growth of machine learning tools to perform forensic analysis, the “lost” information may contain useful data on new or anomalous signals. This data may even help to evaluate degradations in the sensor itself due to temperature or aging effects. This research project is expected to be “data-driven”, making use of large multi-modal datasets available within the research consortium from DSTL and our industry partners to study practical issues in improving information recovery.

The University Defence Research Collaboration are pleased to invite applications for PhD studentships to work as part of a leading team of experts in signal processing.

The project will be hosted by the Institute for Digital Communications at the School of Engineering at the University of Edinburgh and the student will work on the University Defence Research Collaboration (UDRC). The UDRC is a leading research partnership for signal processing for defence and develops new techniques to better transform data across many domains into actionable information, and meet the requirements for improved situational awareness, information superiority, and autonomy. This collaboration, sponsored by Dstl and the EPSRC, is academia-led and has commenced its third phase of research focusing on "Signal Processing in the Information Age". The Consortium is made up of the University of Edinburgh, Heriot-Watt University, Queen’s University Belfast and University of Strathclyde and there are currently PhD opportunities available across the four universities to work on diverse topics in signal processing, as part of a collaborative team. The work will involve strong links with industry and the UK defence sector. The PhD student will be expected to work closely with other research team members and to attend regular meetings to present project updates to the sponsors and partners of this project.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. An undergraduate degree in electronic and electrical engineering, computer science or related discipline. A Masters level qualification is preferred.

Further information on English language requirements for EU/Overseas applicants can be found here -

Funding Notes

Tuition fees + stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate).
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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