ACEMS QUT PhD Scholarship

In this project, the PhD candidate will develop new Bayesian computational algorithms to more effectively monitor submerged shoals off the coast of Western Australia.  Further details about the project are given below.  A scholarship ($30,000 pa stipend) will be provided to the successful candidate for a period of 4 years.

Supervisors: A/Prof James McGree (QUT, ACEMS), Dr Erin Petersen (QUT, ACEMS) and Dr Rebecca Fisher (AIMS)
If you wish to apply for this scholarship, please contact A/Prof James McGree 3138 2313.

Applications close on the 30th June, 2017.  Minimum entry requirements include a first class Honours degree (or equivalent) in statistics or a related area.

The Australian Institute of Marine Science (AIMS) is tasked with monitoring submerged shoals off the coast of North Western Australia. This project will develop new statistical methodology in adaptive Bayesian design to help AIMS monitor more effectively.  There will be three steps to this project.  The first is building a model to describe the variability in the collected submerged shoals data.  This will facilitate an understanding of relationships between variables which may exist, and also provide an understanding of the spatial extent/variability of different habitats, functional groups and species, and how this changes over time.
Secondly, given the model developed in step 1, the following questions will be explored:

  • What impact sizes could be detected (with reasonable power) if AIMS continue with the current monitoring practices?
  • How could the current sampling protocol be changed to gain equivalent (or near equivalent) information but with less resources?
  • If the spatial and temporal variability is not well understood, how can we monitor to learn about both of these?
  • How can we leverage the developed model to monitor more efficiently and potentially gain more information from the same resources?

The final step of the project will be to develop recommendations about how AIMS can implement a cost effective monitoring framework, that accounts for spatial variability and temporal trends in underwater shoal condition, using both fixed sites adaptive sampling of additional shoals.