Development of Machine Learning Approaches to Geotechnical Design of Marine Renewable Energy Foundations

University of Southampton

Faculty of Physical Sciences and Engineering

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.

Supervisory Team: Jared Charles, Susan Gourvenec.

Funding Information

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

Eligibility 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.

Application Process

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 here.

For further information please contact: [email protected]

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.

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

To apply for this PhD, please use the following application link: