A multiscale approach to paleomagnetic analysis of geological materials


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Machine learning applied to magnetic measurements

NPM is teaming up with Andrew Roberts and Hirokuni Oda to explore the possibilities of applying machine learning methods to the analysis of magnetic measurements. Details are below. Please contact Andrew Roberts ( directly for more information.



The appointee will work a part of a team led by Professor Andrew Roberts and Dr Hirokuni Oda on an international project funded by the Japanese National Institute of Advanced Industrial Science and Technology (AIST); “Development of machine learning approaches in magnetic recording and climate research”. 


Position Dimension & Relationships: 

The appointee will contribute to the aims of the project, which involves the application of machine learning to automated processing of experimental rock magnetic data. The appointee will have a specific role within a larger team, which will involve development of Bayesian-based machine learning techniques and the design/deployment of processing software on a cloud computing platform. Additionally, the appointee will be expected to contribute collaboratively to the broader endeavours of the international research group.

Role Statement:

The appointee will be expected to:

  • undertake research under the direction of Professor Andrew Roberts and Dr Hirokuni Oda that contributes to the aims of the project, with a view to development of new machine learning software and contributing to the publication of papers in international refereed journals;
  • collaborate with project team members in developing new machine learning techniques to analyse rock magnetic data;
  • lead the design and development of cloud-based processing software to deploy new data analysis techniques in a form suitable for adoption by the broader scientific community;
  • take responsibility for their own workplace health and safety and not willfully place at risk the health and safety of another person in the workplace; and
  • other duties as allocated by the supervisors consistent with the classification of the position.


  1. A Ph.D. in computer science, machine learning, mathematics, statistics or related discipline, with a track record of independent research as evidenced, for example, by publications in peer-reviewed journals and conference presentations.
  2.  Evidence of an ability to prosecute innovative research in the field of machine learning.
  3. A track record of user-friendly software design, development and maintenance, with a specific focus on deployment on cloud-based platforms.
  4. Excellent oral and written English language skills with a demonstrated ability to communicate and interact effectively with a variety of researchers in an international and cross-disciplinary academic environment and to foster respectful and productive working relationships with staff, students, and colleagues at all levels.
  5. A demonstrated understanding of equal opportunity principles and a commitment to the application of these policies in a University context.
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