PhD Studentship - Profile the Killer Waves in the Ocean with Machine Learning

Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems

How to apply:

How many positions: 1

Funding will cover UK tuition fees and tax-free stipend only (set by the UKVI, £20,780 for 2025/26).

Killer waves or extreme waves are large (> 20 meters tall) and unpredictable surface waves that can be extremely dangerous to ships and other infrastructures. Over twenty-two super-carriers were lost due to collisions with rogue waves in the past decades, causing 525 fatalities. Recently, a better understanding of these extreme waves has become increasingly important and pressing due to the rapid growth of offshore renewable energy as a critical part of the UK’s ambitious plan to achieve net-zero greenhouse gas emissions by 2050. These extreme waves become one of the design driving criteria for the infrastructures of renewable energy devices, as they need to withstand these large forces when hit by extreme waves, whilst keeping the cost affordable.

This PhD project aims to predict what these gigantic waves look like when they appear in the middle of the ocean, where many nonlinear effects take place, such as Benjamin-Feir Instability, spreading, breaking etc. This makes traditional methods particularly difficult to generalise based on all these driving factors, whereas this PhD project takes a novel and explainable data-driven approach – equation-based symbolic machine learning. The aim is to give the industrial widely used NewWave theory (a theory that predicts the linear shape of extreme waves) a modern twist to account for the important nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory to velocity profiles, which will be informative for the design of offshore renewables. Further mathematical analysis of these nonlinear terms will shed light on the question: what generates these extreme waves?

During the PhD, the student is expected to interact with academics across different departments from the University of Manchester, e.g. Offshore Renewable Energy Group, the Hydrodynamics Lab, as well as external institutions and organisations, such as the University of Oxford and other industrial partners.

For further information about the project or any informal enquiries, please contact:

Dr. Tianning Tang, tim.tang@manchester.ac.uk

Dr. Samuel Draycott, samuel.draycott@manchester.ac.uk

Prof Peter Stansby, BA, PhD, DSc, CEng, FICE, FREng, MIAHR p.k.stansby@manchester.ac.uk

Candidates must have a 1st or a high 2i in a degree, ideally at Masters level, in an Engineering subject, Physics, Mathematics, Computer Science or other quantitative background. Knowledge in fluid mechanics, ocean waves, numerical methods and machine learning would be advantageous. The ideal candidate is expected to have a strong interest in nonlinear wave mechanics, be enthusiastic about using state-of-the-art machine learning, be able to have a proactive attitude towards problem solving independently. The student is expected that she/he has prior experience using machine learning tools before and be familiar with signal processing tools.

Interested applicants should contact Dr. Tim Tang via email tim.tang@manchester.ac.uk with an updated CV. Suitable candidates will be asked to complete the electronic application form at The University of Manchester.

Advert information