ARC Linkage Funded Scholarship: Revolutionising high resolution water quality monitoring in the information age

Future student scholarship

Scholarship details

Study levels

Research

Student type

Future students

Study area

All study areas

Eligibility criteria

Academic performance

Application dates

Applications close
17 April 2020

What you'll receive

You will receive a scholarship of $28,420 per year, paid fortnightly as a stipend.

You will also:

  • actively collaborate with researchers in the School of Mathematical Sciences and our Centre for Data Science
  • be given opportunities to travel for presenting at local and international conferences, and for collaborating with other academics on research related to this project.

If you are an international student, you will also receive a tuition fee sponsorship.

Eligibility

To be eligible for this scholarship you must complete or be near completing a:

  • Bachelor of Mathematics (or a related field) with first-class honours
  • Masters by Research in statistics (or a related field).

You will also be assessed on:

  • knowledge and experience with statistical modelling and experimental design
  • statistical programming skills in, for example, R, Python, or C
  • demonstrated capacity to work collaboratively as part of a team, as well as proven ability to use initiative, and to work effectively under supervision
  • demonstrated effective written communication skills
  • oral and interpersonal communication skills
  • knowledge of, or willingness to learn about water quality dynamics in rivers to underpin the development of novel statistical methods
  • willingness to collaborate and engage closely with industry partners.

How to apply

Applicants are encouraged to contact the project supervisor, Associate Professor James McGree prior to submitting an application via email or phone.

How to apply

About the scholarship

Funded by the Australian Research Council through a Linkage Project titled 'Revolutionising high resolution water quality monitoring in the information age', this PhD project will see the successful applicant develop new statistical and computational methods to guide the optimal placement of sensors to monitor water quality across Queensland rivers Australia.

Much of this work will be conducted in interdisciplinary teams, with strong engagement with international collaborators and industry partner organisations. The research will be translated through scientific journal articles, presentations and software products.

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