Name of scholarship/program
Network Science, Optimization and Data Fusion in a Smart City
Eligibility and other criteria
Topic and Aims of the Project
There is a compelling case for training graduates in the mathematical sciences to play a leading role in data-rich applications. Going beyond the fundamental issues of storing, sharing, curating and securing data, mathematicians and operational researchers develop models and algorithms that deliver actionable insights.
This project will drive a new collaboration across the Strathclyde Business School and the Faculty of Science, by combining mathematical ideas from three very active, hot-topic areas
• Network Science: studying not only the individual elements in a system, but also the complex, emergent behaviour that arises when these elements combine,
• Optimization: developing computational strategies for discovering optimal solutions to quantifiable problems, notably on very large data sets, where High Performance Computing is necessary,
• Data Fusion: where heterogeneous sources of data are combined in order to add value, with the relative “influence” and “distinctness” of each data set simultaneously quantified.
The project will be application-driven, with close involvement of Strathclyde’s new Institute for Future Cities ( http://www.strath.ac.uk/business/cities/
). The Institute is designed to work in partnership – integrating and catalysing expertise and research from multiple disciplines within Strathclyde and other academic, government and commercial organisations.
The project will meet the Institute’s aim to tackle the large, complex and difficult issues and opportunities for cities. In order to maximise impact, the precise details will be fleshed out in collaboration with the key data generators and users; notably Glasgow City Council, who administer the £24 Million TSB-funded Smart City Demonstrator award, which is at the heart of the City Observatory, to be housed in Strathclyde’s Technology and Innovation Centre. The essence of this project is to develop new, customized Big Data and High Performance Computing algorithms that can be stress-tested and validated on data from the Observatory. A typical case study for the algorithms would be in combining data sources from the observatory sensors to monitor and predict energy levels across the city, including the new Combined Heat and Power (CHP) systems, with a view to allowing energy storage when demand is low for use during times of higher activity. This has the potential to cut people's fuel bills, help the city in its fight against fuel poverty and give Glaswegians access to affordable warmth.
This PhD project requires a highly numerate graduate with skills and interests in computational science.
* August 31, 2014
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