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New Bayesian computational algorithms for monitoring submerged shoals off the coast of Western Australia

Study level

PhD

Master of Philosophy

Honours

Faculty/Lead unit

Science and Engineering Faculty

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.

Supervisors

Associate Professor James McGree
Position
Associate Professor in Statistics
Division / Faculty
Science and Engineering Faculty

Overview

The Australian Institute of Marine Science (AIMS) is tasked with monitoring submerged shoals off the coast of Western Australia.

This project will develop new statistical methodology in adaptive Bayesian design to help AIMS monitor more effectively.

You will work closely with AIMS and also other members of our ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

Research activities

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 into the future.

Outcomes

The outcomes of the project will include:

  • development of new Bayesian computational algorithms
  • submission of papers to top journals in statistics.

You will develop close connections with AIMS and provide them with recommendations for how they can monitor into the future.

Skills and experience

Ideally, you will have coding experience (for example in Matlab or R), and experience with Bayesian statistics.

Scholarships

You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round

Keywords

Contact

Contact the supervisor for more information.