Available research project

Approximate Bayesian Computation (ABC) for calibrating and evaluating Individual Based Models

Fully funded studentship at University of Reading

Individual-based models (IBMs) are used to simulate the actions of individual animals as they interact with one another and the landscape in which they live. When used in spatially-explicit landscapes IBMs can show how populations change over time in response to management actions. For instance, IBMs are being used to design strategies of conservation and of the exploitation of fisheries, and for assessing the effects on populations of major construction projects and of novel agricultural chemicals.

There is urgent need to improve methods of calibrating such models: existing methods are too slow, and not always accurate. This project aims to improve the best existing method: Approximate Bayesian Computation, ABC. You will work on methodological developments in ABC for high-dimensional parameter spaces and expensive simulators, and put these methods into practice through collaborations with ecologists. Initial focus will be on IBMs developed for fisheries management by Cefas, part of the UK government, https://www.cefas.co.uk/.

This project is part of the new Centre for Doctoral Training in Quantitative and Modelling Skills in Ecology & Evolution (http://www.imperial.ac.uk/qmee-cdt/).

Supervisors

  • Dr Richard Everitt and Prof Richard Sibly (Reading)
  • Dr Robert Thorpe (Cefas)

How to apply

You should send to r.g.everitt@reading.ac.uk:

  1. an extended CV;
  2. a covering letter explaining in detail how you would fit and why you are interested in: the Centre for Doctoral Training in Quantitative and Modelling Skills in Ecology & Evolution (http://www.imperial.ac.uk/qmee-cdt/); and this project in this particular;
  3. the names and e-mail addresses of two academic referees (at least one of them should have supervised you on a previous research project).

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