I am looking for high quality, numerate, candidates to fill these exciting PhD studentships with me as a (co-) supervisor at Loughborough’s School of Business and Economics. Please note that this post has been updated with new links (in blue, below).
The first is modelling dynamic responses to dynamic threats; the second is using analytics in traditional industries. Please see the links below for further details and how to apply. Note that for further information, you will need to click on the blue links below.
One of the most challenging issues for policy makers dealing with bio-security threats is their dynamic nature: diseases may spread quickly and deadly among vulnerable populations and pandemics may cause many casualties.
Finding the appropriate response to threats is a major challenge. Whilst models exist for understanding of the dynamics of the threats themselves, responses can be largely ad-hoc or ‘firefighting’. The aim of this research is to produce robust responses for dynamic threats.
The research will build up as follows, from low to high complexity: static responses to static threats; static responses to dynamic threats; dynamic responses to static threats; and dynamic responses to dynamic threats.
We will use a variety of methods to define the best response: cellular automata, network analysis, spatial modelling, agent-based modelling, and the generation of dynamic fitness landscapes.
This PhD studentship is most suitable for candidates with a background in a quantitative discipline such as management science, operations research, engineering, physics and other natural sciences.
The rise of business analytics has given rise to enormous opportunities within the private sector, but these benefits have yet to be fully realized in public services and regulated industries such as energy, water, and transportation networks. Conversely, governments are mandating collection of data by installing smart metering devices. This gives rise to the need for innovative ways of thinking in industries that are still largely based on traditional economic thinking involving conventional assumptions on optimization and behaviour.
As an example, the energy sector is characterised by strongly defined market structures with incumbents and an ultimate need for energy network security, which not only prevents the quick adoption of technical changes but also translates into regulatory outcomes, such as price caps.
This exciting PhD opportunity will integrate theoretical and empirical approaches and spans two strengths of Loughborough’s School of Business and Economics: microeconomics and particularly rigorous analysis of the determinants of productivity and performance (including cost modelling) and management science (including simulation and network analysis).
We are therefore seeking a student with a quantitative background (whether in economics, management science, engineering, physics or other natural sciences). A willingness to learn new techniques such as, cost modelling, performance measurement, agent-based modelling and network analysis is desired.