Improving GNSS measurements of Australia’s deformation using machine learning  

Project overview

Project title

Improving GNSS measurements of Australia’s deformation using machine learning.  

Project description

This Project will improve the accuracy of estimates of Australia’s 3D motion and deformation using machine learning methods. This will apply new methods to hundreds of Global Navigation Satellite System (GNSS) sites to improve understanding of Australia’s vertical land motion and sea level research. This may improve satellite positioning products used by Australian industry, government and researchers.  

Supervisory team

University

Name of university supervisor	Matt King
Name of university	University of Tasmania
Email address	Matt.King@utas.edu.au
Faculty	College of Sciences and Engineering

CSIRO

Name of CSIRO supervisor	Daniel Smith
Email address	Daniel.V.Smith@data61.csiro.au
CSIRO Research Unit	Data61

Industry

Name of industry supervisor	Anna Riddell
Name of business/organisation	Geoscience Australia
Email address	Anna.Riddell@ga.gov.au

Further details

Primary location of student	University of Tasmania, Churchill Avenue, Sandy Bay TAS 7001, Australia
Industry engagement component location	Geoscience Australia, Corner of Jerrabomberra Avenue and Hindmarsh Drive, Symonston ACT 2609, Australia
Other locations	CSIRO Sandy Bay, 15 College Road, Sandy Bay TAS 7005, Australia
Ideal student skillset	Prior experience with machine learning or statistical techniques. Coding experience, ideally in python. Strong quantitative background in undergraduate studies, such as maths, computer science, engineering, or geodesy. Experience of working in a team environment. A strong background in research as indicated by Honours or Masters results or equivalent industry experience.
Application close date	Open until position filled
Apply	UTAS