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Job Record #15395
TitlePhD studentships in geological modelling using deep-learning
CategoryPhD Studentship
EmployerHeriot-Watt University, UK
LocationUnited Kingdom, Edinburgh
InternationalYes, international applications are welcome
Closure DateFriday, November 30, 2018
PhD studentships in geological modelling using deep-learning techniques

The School of Energy, Geoscience, Infrastructure and Society (EGIS) at
Heriot-Watt University, UK is looking for an excellent PhD candidate to work on
an industrially funded project titled "Geological model generation using
deep-learning techniques". In this project, generative machine learning models
(e.g. GANs among other techniques) will be investigated/adapted to generate
geologically consistent subsurface models while accounting for model
uncertainties. The project will build on the recent work on the topic [1, 2]
and aims to address the questions of learning using limited training data and
handling non-stationary spatial geological models.

Main references:
[1] Shing Chan, Ahmed H. Elsheikh, "Parametric generation of conditional
geological realizations using generative neural networks",
[2] Shing Chan, Ahmed H. Elsheikh, "Exemplar-based synthesis of geology using
kernel discrepancies and generative neural networks",

Essential skills:
-- Master’s degree in machine-learning, computational mathematics, physics or in
a relevant engineering discipline with strong computational skills
-- Programming skills preferably in Python and/or C++
-- Ability to write reports, collate information and present it in a clear and
engaging manner
-- Excellent communication skills

Desirable skills:
-- Machine learning techniques (theory and applications)
-- Computer assisted geological modelling (Object-based, surface-based
modelling, etc.)
-- Background in computational statistics (Spatial Geo-statistics, Bayesian
-- Numerical optimization and nonlinear PDE solvers

Fees and funding:
-- Funding is available to UK/EU/Overseas candidates. It includes tuition fees
and an appropriate stipend for 3.5 years at the EPSRC recommended levels

Application process:
Interested individuals are invited to contact Dr. Ahmed H. Elsheikh
( Please reference the position title when corresponding
about this position and include the following:
-- Cover letter including areas of expertise and research interests
-- Detailed curriculum vitae
-- Degree certificates and transcripts (undergraduate and graduate)
-- Verifiable list of programming skills (e.g. Github repositories)
-- Contact information of academic and/or industrial referees

Contact Information:
Please mention the CFD Jobs Database, record #15395 when responding to this ad.
NameAhmed Elsheikh
Email ApplicationYes
Record Data:
Last Modified14:19:19, Friday, November 16, 2018

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