Deep learning driven ore deposit models

Degree type


Closing date

10 October 2022



Citizenship requirement


About the research project

The transition from a fossil fuel based global energy system to one based on renewable energy sources is urgently needed to limit human induced change to global climate. This transition requires technologies reliant on essential and critical elements, such as copper and molybdenum. Copper is required in increasing quantities for low-carbon energy infrastructure, and critical elements are either in short supply due to their rarity or the projected increases in demand will soon outstrip supply. Despite significant annual investment in exploration and decades of research into how and why ore bodies form, the discovery of new deposits is lagging. However, new methods for modelling the geometric and mineralogical complexities of known ore bodies will help develop more efficient extraction, processing, and waste management protocols. Both the discovery of new deposits and more efficient mining practices provide the resources that humanity needs to affect the "clean" energy transition.

The emergence of machine learning as a viable and scalable approach to integrating, analysing, and generating predictions from large volumes of digital data offers new opportunities for modelling deposit location, shape and characteristics. Deep learning systems have shown their predictive power in data-rich environments, although their use in generating models for exploration and mining has been limited, in part due to the requirements of these systems to have extensive and comprehensive training datasets. The volume and coverage of public and private digital geoscience data companies has reached a point where deep learning is a now an emerging option for ore deposit modelling.

This project will utilise a variety of public and private geoscience databases available through the AMIRA 1249 research project to explore and refine the use of deep learning systems for modelling porphyry copper deposits. The outcomes of this project will form an integral part of the AMIRA P1249 research effort.

Primary Supervisor

Meet Dr Matthew Cracknell


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 $28,854 per annum (2022 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.


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

Additional eligibility criteria specific to this project/scholarship:

  • Honours or Masters degree with a substantial geological and/or geochemical component, or equivalent
  • 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:

  • Proficiency in written and oral English
  • Proficiency in Python (or similar) programming
  • Proficiency in Numerical modelling and machine learning theory and practice

Additional desirable selection criteria specific to this project:

  • Strong background in economic geology, mineralogy, geochemistry and/or geometallurgy
  • Successful completion of BSc level undergraduate subjects in geology, engineering, chemistry, maths and/or physics
  • Previous publications in international peer-reviewed literature and/or conference presentations

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, Dr Matthew Cracknell 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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