A.I. assisted subglacial investigations

A.I. assisted investigation of the East Antarctic subglacial interior (AI-EASE)

Degree type

PhD

Closing date

27 March 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, such applications have recently been used to make highly resolved topographic realisations of bedrock and water pathways beneath ice sheets (MacKie et al. 2020). Related applications have also advanced our understanding of subglacial geology and geothermal heat flow in Antarctica where observations are scarce (Stål et al. 2021). More recently, neural networks have been used to emulate physical glacier models with significant computational advantages (Jouvet et al. 2022). This project combines glaciological research in IMAS and the Compute Earth group in the School of Natural Sciences, with the aim of improving our understanding of the basal interior of the East Antarctic Ice Sheet. The work will involve artificial neural networks trained with observational data from Antarctica as well as outputs from numerical ice sheet models.

The overall aim of the research is to understand how the East Antarctic Ice Sheet is shaped and influenced by the largely unknown continent beneath it. The work is specifically designed to resolve the flow of water at the ice-bed interface (Mackie et al. 2020), the distribution of soft sediments connected to deep groundwater systems (Christoffersen et al. 2014), and the formation of temperate basal ice with low viscosity (Law et al. 2023).  With a broad scope and focus on drainage basins across East Antarctica, the research will identify poorly understood factors that control the flow of ice on timescales ranging from decades to centuries.

References:

  • Christoffersen et al. (2014). doi:10.1002/2014gl059250
  • Jouvet etl al. (2022). doi:10.1017/jog.2021.120
  • Law et al.  (2023). doi:10.1126/sciadv.abq5180
  • MacKie et al. (2020). doi:10.1029/2019JF005420
  • Reichstein et al. (2019). doi:10.1038/s41586-019-0912-1
  • Stål et al. (2021). doi:10.1029/2020GC009428

Primary Supervisor

Meet Prof Anya Reading

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.

This project is part of the ARC Australian Centre for Excellence in Antarctic Science (ACEAS). Candidates will be considered and assessed for ACEAS top-up scholarship eligibility (valued at $5,000 per annum for 3.5 years) upon ranking of the applicants by our GRCs and alignment of the project with ACEAS.

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

Eligibility

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

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 Earth science, geophysics, physics, mathematics, or a related discipline
  • A passion for Antarctic studies
  • Excellent 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 other coding language

Additional desirable selection criteria specific to this project:

  • Previous experience in spatial applications of statistics including deep learning or machine learning applications
  • Theoretical understanding of glaciological processes from coursework or research; previous experience using geoscientific or glaciological data

Application process

There is a three-step application process:

  1. Select your project, and check you meet the eligibility and selection criteria;
  2. Contact the Primary Supervisor, Prof Anya Reading, to discuss your suitability and the project's requirements; and
  3. Submit an application by the closing date listed above.
    • 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.
    • As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.

Following the application 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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