PhD Studentship - Development of machine learning approaches to geotechnical design of marine renewable energy foundations

PhD Studentship - Development of machine learning approaches to geotechnical design of marine renewable energy foundations

United Kingdom 31 Aug 2021
The University of Southampton

The University of Southampton

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State University
Host Country
31 Aug 2021
Study level
Opportunity type
Opportunity funding
Full funding
Eligible Countries
This opportunity is destined for all countries
Eligible Region
All Regions

School of Engineering


Boldrewood Campus

Closing Date:  

Tuesday 31 August 2021



Supervisory Team:    Jared Charles, Susan Gourvenec

 Project description

A key avenue for mitigating the impacts of climate change is transitioning to renewable energy generation. Offshore windfarms are a key component in this transition and attract significant investment both in the North Sea and more recently in numerous sites around the world.

 With the turbines themselves increasing in capacity and size, and larger, more challenging offshore sites being considered for development, it is essential that design methodologies, not just for individual turbines and their foundations, but for the entire site continue to evolve.

 A successful applicant to this PhD project will have a drive to use their strong programming skills to apply machine learning and optimisation techniques to a geotechnical engineering problem that will have on-the-ground impact.

 This project will involve developing tools that allow the incorporation of geotechnical and foundation design concepts into windfarm design and layout optimisation. Opportunities will exist to carry out laboratory work, including on Southampton Universities 130G geotechnical centrifuge, perform both analytical and numerical modelling, and work with site investigation data from real windfarm projects.

 You will be based at the National Infrastructure Laboratory on the University’s Boldrewood Innovation Campus with access to the Geotechnical Lab, Geotechnical Centrifuge Facility, Hydrodynamics Facility and maker spaces. This project forms part of the activities of a Royal Academy of Engineering Chair in Emerging Technologies for Intelligent & Resilient Ocean Engineering.

 If you wish to discuss any details of the project informally, please contact Jared Charles, Infrastructure Research Group, Email:

 Entry Requirements

A First Class or high 2:1 Degree in Engineering or Computer Science. Strong programming skills essential. Experience or interest in geotechnical, foundation, offshore or wind engineering desirable. Experience or interest in Machine Learning, Neural Networks, Genetic Programming or Optimisation desirable.

Closing date : applications should be received no later than 31 August 2021 for standard admissions, but later applications may be considered depending on the funds remaining in place.

 Funding: For UK students, Tuition Fees and a stipend of £15,285 tax-free per annum for up to 3.5 years. 

How To Apply

Applications should be made online. Select programme type (Research), 2021/22, Faculty of Physical Sciences and Engineering, next page select “PhD  Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Jared Charles

 Applications should include

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online:

 For further information please contact: 

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