Employment Opportunities

ACEMS Postdoctoral Fellow Positions at UNSW

ACEMS-UNSWThere are 2 postdoctoral positions available at UNSW, one being in the School of Mathematics & Statistics, Faculty of Science and the other in the School of Economics at the UNSW Business School. Both positions will have the opportunity to interact with members of both Schools.

These positions are supported by, and are an integral part of ACEMS - the Australian Centre of Excellence for Mathematical and Statistical Frontiers – a multi-institution research centre for leading research in mathematics, statistics and machine learning.

About the role

  • A$89K – A$95K per year (plus 17% superannuation and leave loading)
  • 2 Years fixed-term
  • Full time position

The appointees will undertake collaborative and self-directed research on methods for the analysis of large and complex datasets, and their application. Methodological development is expected to be wide ranging, but will involve techniques such as symbolic data analysis, methods for computationally intractable likelihood functions (such as pseudo-marginal methods, approximate Bayesian computation and synthetic likelihoods), variational Bayes and non-parametric methods.

Applications will include (but are not limited to) large panel datasets, satellite imagery and complex and nonstandard ecological and environmental datasets, and evidence accumulation models for decision making in psychology and business.

To be successful in these roles, you should have:

  • PhD in Statistics or equivalent subject, with strong computational or methodological component
  • Strong computer programming skills in e.g. R, Python, Matlab, C++, etc
  • Excellent oral and written communication skills
  • Proven, or demonstrated excellent potential for, an outstanding publication record
  • Demonstrated high level analytical and problem solving skills
  • Demonstrated capacity to deliver high quality project outcomes in a timely manner
  • Demonstrated ability to work as part of a team and work with minimal supervision.

You may be required to undergo pre-employment checks prior to appointment to these roles. You
should systematically address the selection criteria listed within the position description in your
application. Please apply online - applications will not be accepted if sent directly to the contacts
listed.

Applications close: 5 May 2017
For full details of this position and application procedure, go to http://www.jobs.unsw.edu.au/ click “job search” and look for position Ref 58115.

Further information:

Professor Scott Sisson
Email: Scott.Sisson@unsw.edu.au

Scientia Professor Robert Kohn
Email: r.kohn@unsw.edu.au

 

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 james.mcgree@qut.edu.au/(07) 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.