Ice shelf deep learning

A deep learning emulator for investigation of Antarctic ice shelves

Degree type

PhD

Closing date

25 September 2023

Campus

Hobart

Citizenship requirement

Domestic / International

About the research project

Deep learning with artificial neural networks has evolved rapidly and become a widely applied tool for the study of Earth surface processes (Reichstein et al. 2019). In glaciology, neural networks have been used to emulate physically advanced glacier models, speeding up the computational simulation by several orders of magnitude (Jouvet et al. 2022). This efficiency gain makes deep learning a promising new tool for assessment of ice shelves, which are the floating extension of the Antarctic Ice Sheet.

This project will use machine learning to understand how ice shelves in Antarctica behave and interact with the ocean and climate. The student will simulate ice shelves using the Instructed Glacier Model (Jouvet et al. 2022) which emulates the physics of ice shelves. Upon configuration and training, the emulator will be used to investigate ice shelves across Antarctica with the aim of identifying which ones are fragile and which ones are stable over the coming decades and century.

Specifically, the research will:

  1. Use a physics-informed emulator to simulate the flow of ice shelves in East Antarctica
  2. Explore the emulator model's ability to reproduce ice shelves as observed
  3. Assess ice shelf stability of East Antarctic ice shelves under different environmental forcing scenarios

With a modern approach and broad scope, the research will advance our understanding of ice shelves, which have disappeared almost entirely in Greenland (where climate is warmer) but still play a major role for the stability of the Antarctic Ice Sheet (Fürst et al. 2016).

References:

  • Fürst, J et al. (2016). The safety band of Antarctic ice shelves. Nature Clim Change 6, 479–482.
  • Jouvet G et al (2022). Deep learning speeds up ice flow modelling by several orders of magnitude. Journal of Glaciology 68, 651–664.
  • Reichstein M et al. (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204.

Primary Supervisor

Meet Prof Poul Christoffersen

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

  • a living allowance stipend of $31,500 per annum (2023 rate, indexed annually) 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)

If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.

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 be considered for a top-up scholarship of $6,000 per annum for 3.5 years. This scholarship is funded from the Australian Government as part of the Antarctic Science Collaboration Initiative program through the Australian Antarctic Program Partnership (AAPP).

Other funding opportunities and fees

For further information regarding other scholarships on offer, and the various fees of 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.

Additional eligibility criteria specific to this project/scholarship:

  • Applicants must be able to undertake the project on-campus

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:

  • BSc (Hons) or Master's degree in Antarctic studies, Earth science, geophysics, maths or a related discipline
  • A passion for Antarctic science
  • Good scientific communication skills, demonstrated by the production of a thesis or published manuscript and seminars, or an interview
  • Ability to manage data in Python or another coding language

Additional desirable selection criteria specific to this project:

  • Previous experience in spatial applications of statistics, including deep learning or machine learning
  • Previous experience in numerical modelling or managing data
  • Theoretical understanding of glaciological processes from coursework or research

Application process

  1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
  2. Contact Prof Poul Christoffersen 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 25 September 2023.

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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