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GEOPHYSICS Home

GEOPHYSICS  December 2018

GEOPHYSICS December 2018

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Subject:

PhD in Deep Learning for Geophysical Inversion and Imaging at Curtin University

From:

Chris Elders <[log in to unmask]>

Reply-To:

Chris Elders <[log in to unmask]>

Date:

Fri, 14 Dec 2018 07:07:40 +0000

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PhD in Deep Learning for Geophysical Inversion and Imaging

Curtin University is looking for outstanding candidates to apply 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. 

Project 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.

Qualification requirements
•	The project primarily involves performing computational experiments and developing new simulation tools. Candidates with background in mathematics / engineering / physics / computer science or background in geophysics with strong computational experience are encouraged to apply.
•	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 (http://futurestudents.curtin.edu.au/research/apply/admission-criteria/).

Other qualifications
•	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 highly desirable.

Personal characteristics
•	We seek independent and highly motivated PhD-candidates, eager to tackle new problems and conduct innovative work that makes real-world difference.
•	The candidate should have good communication skills and the ability to communicate well with both mentors and peers.

Job description
•	Perform computer modelling with applications in geophysical and geological processes in collaboration with supervisors and colleagues.
•	The successful candidate will be involved in the publication of journal articles and conference presentations. The research will involve collaborations with top-tier institutions in Australia, the USA and Europe.

Prospective candidates should contact Dr. Vladimir Puzyrev ([log in to unmask]) for further information. The applicants should provide CV, academic transcript, personal statement, explaining why they are interested in the PhD project, and contact details of two referees.

The scholarship provides 3 years of funding without teaching duties. The start date is flexible. The position will remain open until filled. Early application is encouraged.

About us
Curtin University is ranked in the top 1% universities worldwide (ARWU 2018 and QS World University Rankings 2019), and holds 2nd place in the world for Mineral and Mining Engineering (QS World University Rankings by Subject). Based in Perth, with additional campuses in Singapore, Malaysia, Dubai and Mauritius, Curtin is a global university known for high-impact research, strong industry partnerships and commitment to preparing students for jobs of the future. Curtin Centre for Oil and Gas Innovation (COGIC) brings Curtin University’s multidisciplinary research and development expertise to the oil and gas industry’s technology challenges.

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