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Big data and IOT (Internet of Things) application for sustainable infrastructure, industrial and energy performance

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  • Full or part time
    Prof N Dawood
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The industrial and infrastructure sector accounts for over a half of the total energy use in the European Union. In order to meet carbon reduction targets, sustainability and high efficiency is needed across the entire energy supply chain from the point of generation through to the point of end use in industry. Modern Information and Communication Technologies (ICT) and big data management techniques have the potential to make significant contributions to sustainable systems.

Applications are invited for fundamental or applied research projects investigating the use of industrial informatics and big data in smart grid/smart factory applications. Projects should principally focus upon ICT aspects of sustainable infrastructure and should include one or more of the following areas: communication and control infrastructures; situation awareness and visualisation; predictive analytics; distributed energy control and optimization; energy-aware manufacturing and demand response.

Funding Notes

The studentship will cover tuition fees and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress.

Teesside University is part of the Doctoral Training Alliance (DTA), a doctoral training initiative from University Alliance. There are currently three DTAs; Applied Biosciences for Health (launched 2015), Energy (launched 2016) and Social Policy (launched 2017).

Developed by Alliance universities, the DTA builds on our research strengths and industry-focused ethos. The training will produce independent, highly-employable researchers with knowledge, expertise and skills in strategically-important research areas. It is the largest multi-partner and only nationwide initiative of its kind.

How good is research at Teesside University in General Engineering?

FTE Category A staff submitted: 13.00

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

Click here to see the results for all UK universities

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