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PhD Studentship - Stochastic systems approaches to disease control

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  • Full or part time
    Dr R Davidson
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Applications are sought from exceptional candidates with backgrounds in mathematical or related disciplines, for the above PhD position jointly offered by the University of York, SRUC and Biomathematics and Statistics Scotland (BioSS). The studentship, based at BioSS in Edinburgh, provides the opportunity to acquire highly transferrable skills in state-of-the-art methods for stochastic and statistical modelling, and will develop models and theory to inform systems approaches to disease control.

Although much is understood about the epidemiology of transmissible diseases of key importance to the livestock sector, many endemic infections remain stubbornly persistent at the national scale. In recent years much policy research has investigated the role that low cost measures (‘nudges’) can have on influencing behaviour in a wide range of systems. In the context of disease control a number of behaviours, with distinct patterns of variation between farms e.g. management decisions and biosecurity measures, could be suitable targets for such an approach.

This project aims to develop systems approaches using stochastic models and analysis to explore how relatively small changes at the individual farm level might be coordinated to produce large effects, reducing persistence and prevalence of endemic disease at the national scale. The extent to which such changes would make the livestock sector more resilient to exotic disease incursions will also be explored. A key aim is to develop analytic and simulation tools for understanding and control of complex systems which account for the role of variation in agent behaviour across the system. The insights, approaches and methods developed are therefore likely to be applicable to many complex problems.

In addition to mathematical abilities (with a 2:1 or higher in mathematics or related discipline), candidates should be in possession of strong IT skills and be able to demonstrate the ability to communicate research both at a general level and to scientists from a range of disciplines. It is anticipated that the successful candidates will foster strong links across all of the research groups involved as well as more broadly with other partner organisations and beyond. Experience with any of stochastic processes, dynamical systems, complex systems, Bayesian statistics, MCMC, R and C++, as well as experience working with and understanding research literature will all be highly beneficial. It is expected that the students will present their work at national and international conferences, as well as attending workshops and summer schools.
The stipend for this studentship is £14,057 pa (subject to revision in 2016) over three years, with an anticipated start date of 1st September 2016 (although the position is available from January 2016 and a different start date is negotiable depending on circumstances). The successful applicant will be based at BioSS in Edinburgh, registered for a PhD at the University of York, and will be expected to make frequent visits to all partner institutions.

To discuss the position informally, potential candidates are invited to contact Prof. Glenn Marion ([Email Address Removed]) or Dr Ross Davidson ([Email Address Removed]).


To apply please fill out the online application form available at including a CV and covering letter explaining why you are suited to the post. Alternatively application packs can be requested from [email protected] Tel 0131 535 4028 quoting reference SRUC/10431/biosecurity

The closing date for the return of applications is 31st January 2016. We expect to hold interviews on Friday 5th of February.

How good is research at SRUC - Scotland’s Rural College in Agriculture, Veterinary and Food Science?
(joint submission with University of Edinburgh)

FTE Category A staff submitted: 57.37

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

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