Postdoctoral Research Fellow - Statistical Learning and Geostatistics

This new full time post is available immediately on a fixed term basis until 31 August 2027.

The post

The Faculty wishes to recruit a Postdoctoral Research Fellow to develop research in computational methods for geological modelling, geostatistics, mine planning and predictive geometallurgy. This post is funded by the Mark Cutifani / Anglo American Chair in Mining Innovation led by Prof. Julian Ortiz, and is available immediately. The successful applicant will propose, lead and participate in research projects, participate in the supervision and mentorship of post-graduate students, publish and disseminate research results, and participate in the development of a research programme in these research areas.  

The post will include:

• Undertaking research in statistical learning and geostatistical methods to model earth science variables, in the context of the mining value chain.
• Contributing to teaching and learning programmes at Camborne School of Mines.
• Supervising of postgraduate students.
• Acting as a research team leader, routinely communicating knowledge and research results to broad audiences.
• As determine by the nature of the projects and at the direction of the PI, to plan, coordinate and implement research program activities.

About you

The successful applicant will be able to develop research objectives, projects and proposals; identify sources of research funding and contribute to the process of securing funds and make presentations at conferences and other events.

Applicants will possess a relevant PhD or equivalent qualification/experience in Geology, Geological Engineering, Mining Engineering or Mineral Processing or a related field of study. The successful applicant will be a nationally recognised researcher in geological or mining engineering and possess sufficient specialist knowledge in the discipline to develop research programmes and methodologies, with proper expertise in Python or similar programming language, and a strong background in Mathematics and Statistics, and Scientific Programming. The successful applicant will also be able to work collaboratively, supervise the work of others and act as team leader as required.

Applicants will be able to develop mathematical and statistical learning methods for geostatistics, optimization and forecasting; code advanced algorithms for numerical modelling of earth science data and mining processes; develop, adapt and apply artificial intelligence methods for optimization and predictive modelling; prepare conference presentations and papers for journal publication, tutor postgraduate students in coding, modelling and mining engineering.

Please ensure you read the Job Description and Person Specification for full details of this role.

Further information

For further information please contact Prof. Julian Ortiz-Cabrera, e-mail j.ortiz-cabrera@exeter.ac.uk.

Interviews are expected to take place on 19 to 23 August 2024.

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