Resolving glacier and solid Earth interactions in the Antarctic Peninsula with machine learning and numerical modelling

About the research project

This project will develop new knowledge of how often-rapidly thinning glaciers in the Antarctic Peninsula impact the solid Earth beneath the ice, revealing the Earth's viscoelastic properties. Advancing knowledge of this process, known as glacial isostatic adjustment, is a key to understanding past, present, and future sea level rise.

To do this, it will leverage a globally unique natural experiment driven by the 2002 collapse of the Larsen B Ice Shelf in the Antarctic Peninsula. This led to the almost immediate onset of glacier thinning, of up to 30 metres per year, and then rapid uplift of the solid Earth due to viscoelastic deformation. This experiment enables us to understand the solid Earth’s response to changes in surface loading, testing competing ideas.

The project will first use machine learning, likely physics-informed neural networks, to create a monthly record of elevation change of the glaciers feeding the Larsen B Ice Shelf. This part of the project will use extensive but intermittent data on ice elevation, grounding line location and velocity, combined with the theory of the physics of glaciers.

In a second phase, it will use these data in a new model of the solid Earth’s viscoelastic response to surface loading, G-ADOPT, to test different theories (rheological models) of how the Earth responds to glacier loading changes.

This is an exciting project that will build skills in machine learning, data handling, numerical modelling and create fundamental new knowledge on how the solid Earth works. This has implications for our understanding of past, present and future ice sheet and sea-level change.

This project is a part of Prof Matt King’s ARC Laureate Fellowship, which will build a team of 10-15 researchers focused on tracking and predicting change in the East Antarctic ice sheet. While this project’s focus is on the Antarctic Peninsula, the knowledge it creates will inform other parts of the project. The PhD student will also work closely with a postdoctoral researcher working on modelling glacial isostatic adjustment using G-ADOPT.

The project includes a top-up scholarship of $5000 per year for up to four years, and support for conference attendance and professional development.

References:

https://www.sciencedirect.com/science/article/pii/S0012821X14002519

https://academic.oup.com/gji/article/231/1/118/6561618

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2025GL114595

https://academic.oup.com/gji/article/222/2/1013/5835685

Primary supervisor

Meet Professor Matt King

Funding

The successful applicant will receive a scholarship which provides:

• a living allowance stipend co-funded with ARC of $34,315 per annum for 3.5 years
• a relocation allowance of up to $2,000
• a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

A tuition fee offset may be offered to eligible international applicants following competitive assessment 

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Additional Funding

If successful, applicants will also receive a Laureate Fellowship PhD top-up top-up scholarship of $5,000 per annum for 3.5 years. This scholarship is funded from ARC.

If successful, international applicants will receive Single Overseas Health Cover (OSHC).

Other funding opportunities and fees

For further information regarding other scholarships on offer, and the various fees for undertaking a research degree, please visit our Scholarships and fees on research degrees page.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Ensure your eligibility for the scholarship round by referring to our Key Dates.

Selection criteria

The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

• Strong mathematical research background, such as a major in mathematics/statistics, physics, engineering, or geodesy.
• Experience in coding, ideally in python

Additional desirable selection criteria specific to this project:

• Experience of working with statistical learning/machine learning, including physics-informed machine learning
• Experience of working with large datasets
• Experience of working in a Linux environment

Application process

1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
2. Contact Professor Matt King to discuss your suitability and the project's requirements; and
3. In your application:
   • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
   • Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
4. Apply prior to 1 August 2026.

Full details of the application process can be found under the ' How to apply ' section of the Research Degrees website.

Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

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