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Deep Learning for Geophysical Inversion and Imaging (COGIC) PhD Scholarship in Australia 2019

Curtin University is accepting applications from outstanding candidates for a PhD position in deep learning for geosciences.

Deep learning methods have established their great potential in various areas including computer vision, speech recognition, robotics, autonomous vehicles, and many others. When applied to geophysical data, these techniques have the potential to completely transform various tasks in data processing, imaging, and interpretation.

Details

This PhD project will explore the latest developments in deep learning and data analytics for inversion of large-scale geological models. The traditional inversion is usually posed as a nonlinear optimization problem. Its solution is computationally challenging due to the expensive computational cost of simulations and multiple local minima present in all realistic problems.
This project will focus on alternatives to traditional geophysical inversion by leveraging deep neural networks, in particular, generative adversarial networks. The aim is to reduce computational time in both forward and inverse simulations, as well as to assist in uncertainty quantification.

Award Value

  • The scholarship provides 3 years of funding without teaching duties.
  • The living stipend is paid by Curtin University through COGIC according to the Australian Government RTP scholarship base rate of AU$27,596.00 (2019), tax-free, per annum.

Qualifications

  • Applicants must hold a Master’s or first class Honours degree in Science or Engineering or equivalent, have an excellent academic track record and meet the University admission criteria
  • The project primarily involves performing computational experiments and developing new simulation tools.
  • Candidates with a background in mathematics/engineering/physics/computer science or background in geophysics with strong computational experience are encouraged to apply.
  • Good written and oral English language skills.
  • Relevant experience in geophysical/geological modelling is beneficial.
  • Experience with scientific publication is an advantage.
  • Experience in machine learning frameworks, C++/Python, parallel computing or data analysis is beneficial as well.

Application Procedure

The applicants should provide:

  • CV
  • Academic transcript
  • A short personal statement, explaining why they are interested in the PhD project
  • Contact details of two referees

Applications Deadline: Early application is encouraged.

Scholarship Link

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