The SAFARI Project is a major industry funded JIP which focuses on novel methods for acquiring and studying reservoir analogue data. Over the past 10 years the project has worked extensively with clastic depositional systems and the next phase of the project will include an expansion into Carbonates and Structural Geology, using the workflows and approach developed previously, especially the extensive use of Virtual Outcrop techniques pioneered by the Group. The project is also keen to integrate recent development in machine learning into its work flows.
In recent years, there has been a proliferation of new machine learning algorithms for dealing with very large and complex datasets. These methodologies are now extending into the geosciences and there is considerable interest in applying them. Recent advances in acquisition and processing of Virtual Outcrop Data has produced large volumes of 3D image data which potentially lend themselves to semi-supervised, automated interpretation using neural networks trained on previous interpretations. There have been significant advances in automated image interpretation in recent years especially within the medical fields using Statistical Shape Models (e.g. Lindner et al. 2017, Foran et al. 2013). The goal of this study would be to work with the geologists on the project to test and develop various methods to produce robust interpretations of our unique in house database.
The project is suited to a computer science/data science graduate who is keen on working with image interpretation in a 3D environment. No prior knowledge of geology or sedimentary systems is required.
The PhD student will join an active a vibrant research project which is supported by 14 Companies and involves a large group of PhD students and researchers. The study will be undertaken in collaboration with Computer Scientists at the University of Aberdeen and an external supervisor.
Candidates should have (or expect to achieve) a minimum of a UK Honours degree at 2.1 or above (or equivalent) Data science. A strong mathematical background is essential as is an ability to code.
A willingness to step out of your subject area and to collaborate with people in other disciplines is essential.
The other supervisor on the project is Mr Ben Bamford, 57Degrees Machine Learning and AI
• Apply for Degree of Doctor of Philosophy in Geology
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘SAFARI’ as Intended Source of Funding
• State the exact project title on the application form
Application closing date is 12:00pm (GMT) on 1 April 2019. Applications received after this time will NOT be considered. Additionally, incomplete applications will NOT be considered.
When applying please ensure all required documents are attached:
• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• 2 References (Academic, where possible - we will not be contacting referees)
Informal inquiries can be made to Professor J Howell ([email protected]
) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ([email protected]
The start date of the project is 1 October 2019