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EPSRC PhD studentship in: Treatment to Tap – securing water quality by extracting value from high resolution sensor network data

Posted: 20.05.2019

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4-years tax-free stipend of £19,000 per year and all tuition fees paid

 

A partnership between The University of Sheffield, Sheffield Water Centre and Siemens, as part of the Water Infrastructure and Resilience EPSRC Centre for Doctoral Training

 

Closing Date for Applications: 21st June 2019

Start Date: 30th September 2019 (contract duration 4 years)

Interview Date: 9th July 2019

 

This fully funded project provides the opportunity to work with internationally unique data sets from live drinking water distribution systems accessible via state of the art internet of things platform. The aim will be to derive value from data, informing proactive management of ageing infrastructure, safeguarding drinking water quality and ultimately public health.

 

‘Digital’ is set to transform resilience for the water sector, but action is needed to realise the potential. While we are able to quantify the costs of instrumentation, communications etc., we do not know what information can be derived from the data collected and hence the value that can be obtained. The benefits arising from increasing instrumentation, and an increasing range of parameters, are limited if the current uses of data are continued as the template for the future. This project will explore development and validation of analysis techniques to evidence both how to best deploy and manage such sensor networks, and the value that can be derived from resultant data. Aspects that may be explored include: Optimising instrumentation numbers and locations; Automated data driven analysis for event and feature detection; Application of deep learning techniques to identify precursor information.

 

The PhD will benefit from being part of WIRe (Centre for Doctoral Training in Water Infrastructure and Resilience). WIRe is a collaboration between the three leading UK Universities in water resilient infrastructure. Students will benefit from a bespoke training scheme delivered by world leading authorities from academia and industry, access to world leading experimental and computational facilities as well as close and regular contact with industry and end user partners. Resources are also available for international collaboration and conference attendance. WIRe is committed to promoting a diverse and inclusive community, and offer a range of family friendly, inclusive employment policies. For further information on the CDT WIRe scheme visit the web site at: https://cdtwire.com/.

 

Supervision

The project will be supervised by Prof Joby Boxall and Dr Stewart Husband at the University of Sheffield in collaboration with Siemens global water IoT application centre based in Manchester. This centre leads a digitalisation development initiative within the water sector focussed on value generation from big datasets. The normal place of work is expected to be at the University of Sheffield, with close regular interaction with Siemens and periods at Southern Water (Brighton) for further field trials, with travel and subsistence costs provided for these periods.

 

Eligibility Criteria

Normal EPSRC funding eligibility applies to this award, so students must have a relevant connection with the UK (usually established by residence).

 

Selection Criteria

The selection criteria are a good first degree in relevant engineering, maths or science discipline with experience of computational analysis techniques and enthusiasm for the topic area.

 

How to apply

Interested candidates should email a covering letter and their Curriculum Vitae to Lindsay Hopcroft (l.hopcroft@sheffield.ac.uk).

For information and informal enquiries please contact: Prof. Joby Boxall (j.b.boxall@sheffield.ac.uk)

 

https://www.findaphd.com/phds/project/epsrc-phd-studentship-in-treatment-to-tap-securing-water-quality-by-extracting-value-from-high-resolution-sensor-network-data/?p109737

 

https://www.jobs.ac.uk/job/BSH392/epsrc-phd-studentship-in-treatment-to-tap-securing-water-quality-by-extracting-value-from-high-resolution-sensor-network-data

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