We would expect the candidate to have a First Class or Upper Second Class Honours degree in a relevant area of mathematical sciences (e.g. Statistics, Mathematics, Machine Learning, Computer Science, Engineering, Geostatistics, Data Science, Geophysics, Physics), and to have some experience of programming in Python and/or R. Previous experience with Bayesian statistics, Gaussian processes and geophysical data would be an advantage.
Sponsored by Energy Technology Partnership (ETP), Ørsted and University of Strathclyde, this joint university/industry PhD offers an exciting opportunity to undertake research on novel statistical and Bayesian machine learning methods for ground modelling, supported by a multi-disciplinary team of academics from two institutions (University of Strathclyde and University of Glasgow) and industry supervision from a leading offshore wind developer (Ørsted).
The research is aimed at advancing the state-of-the-art in automated ground modelling of offshore wind farms. There are many sources of information which are collected to characterise the ground of an offshore wind farm site (e.g. geophysical and geotechnical data). This project seeks to develop a rigorous, statistical framework to automatically combine these information to improve the quality of the ground model for an offshore wind farm site. This will be achieved using statistical and Bayesian machine learning techniques, including conditional autoregressive (CAR) and multi-output Gaussian process (GP) models.
In addition, the project will develop novel algorithms that use the integrated ground model to optimise the planning of new site investigation (SI) to collect more information to improve the quality and reduce the uncertainty of the ground model. As ground modelling and SI planning are important components of most building and construction projects, the skills acquired in this project will be in demand across a broad range of industries such as offshore wind, oil and gas, tunnelling etc. Furthermore, the advanced data science skills acquired in this project are highly valued and will be in demand across most industries.
This project is suitable for a candidate who wishes to conduct applied research that makes an immediate impact in the real world, and has a strong interest in statistics and Bayesian machine learning.
The successful candidate will be based primarily at the Department of Civil and Environmental Engineering, University of Strathclyde. The candidate will be jointly supervised by Dr Stephen Suryasentana and Prof Zoe Shipton (Department of Civil and Environmental Engineering, University of Strathclyde), Dr Craig Anderson (School of Mathematics and Statistics, University of Glasgow) and Prof John Quigley (Department of Management Science, University of Strathclyde). Furthermore, the candidate will work closely with the industry sponsor (Ørsted), who is the world's largest developer of offshore wind power. The candidate will receive guidance from Ørsted’s technical specialists and gain significant experience in how ground modelling is carried out in the offshore wind industry. The unique combination of academic and industry contacts will be highly beneficial to the candidate’s learning and career development, and future employability.
Fully-funded scholarship for 3.5 years covers all university tuition fees and an annual tax-free stipend of £15,285 for UK students. International students are also eligible for the scholarship, but they would need to find other funding sources to cover the university tuition fee difference between the Home rate (£4,407 per annum) and the International rate (£20,900 per annum).
PhD Supervisor: Dr Stephen Suryansentana
Department Administrator: email@example.com
How to apply
Please send your application (and any informal inquiries) to Dr Stephen Suryasentana by 5pm on Sunday 31 Jan 2021. Your application should include the following:
- A cover letter of at most two pages explaining why you are interested in the project and what skills and ideas you believe you would contribute to the project
- An up to date curriculum vitae (CV)
- Evidence of a first class or upper second class honours degree or a Master degree (or equivalent) in subjects relevant to statistics, computer science, machine learning, engineering, geostatistics, geophysics, physics or mathematics.
It is recommended to apply early as interviews will be carried out on a rolling basis until the position is filled.