🎓 Discover PhD and Master's programmes at leading universities worldwide — Sign up free to save searches and get email alerts
LU

Parameterising wakes for oceanographic models

Loughborough University Offshore Wind CDT

PhD project focused on developing advanced parameterisations of offshore wind turbine wakes for oceanographic models, combining CFD simulations and marine modelling to understand environmental impacts of large-scale wind farm expansion.

Project Description

This PhD is part of the EPSRC-funded Offshore Wind CDT, a collaboration between multiple UK universities including Loughborough University, Durham University, University of Hull, and University of Sheffield. The project is supported by Centre for Environment, Fisheries and Aquaculture Science (CEFAS) and the National Oceanography Centre (NOC). The research addresses the growing need to understand how offshore wind farms influence oceanographic processes. Wind turbine wakes alter atmospheric flow, which affects sea-surface conditions and ocean circulation. Current oceanographic models do not adequately capture these effects. The project aims to improve sea-surface wake parameterisations for use in ocean models such as FVCOM. Key tasks include: Literature review and dataset collection CFD simulations of atmospheric wakes Development and validation of wake parameterisations Application to North Sea oceanographic modelling Students will complete a 6-month training programme at the University of Hull before continuing research at Loughborough University, with placements at the National Oceanography Centre.

Entry Requirements

First-class degree OR 2:1 with Master’s OR Master’s with distinction
Background in engineering, physics, mathematics, or environmental science
Strong knowledge of fluid mechanics or physical oceanography
IELTS 7.0 (if applicable)

How to Apply

Applications are accepted on a rolling basis for September 2026 entry. Candidates are encouraged to apply early as interviews and offers may be made before the deadline.

Eligibility

UK/Home
EU
International

Supervisor Profile

DC
Dr Charlie Lloyd, Prof Robert Dorrell, Dr Majid Bastankhah, Dr Michela De Dominicis
Loughborough University, Offshore Wind CDT

Related Opportunities

Prediction of Forever Chemical Concentrations in Drinking Water Treatment Systems
Cranfield University Dr I Carra 🎓 Analytical Chemistry 🎓 Environmental Engineering

Develop a predictive model to forecast PFAS concentrations in drinking water treatment systems. Focus on optimizing granular activated carbon filters to reduce costs and support regulatory compliance. Gain valuable expe…

This research targets a critical environmental and public health challenge by improving PFAS removal from drinking water. The project suppo…

1657+ citations · h21
water
Nano-enabled detection and risk communication of microplastics in ASEAN food chains
Monash University Malaysia Dr Athirah Bakhtiar 🎓 Environmental Chemistry 🎓 Environmental Engineering

Develop novel nanomaterial-based sensors to detect microplastics in the ASEAN food chain and use digital media analytics to design effective public risk communication strategies. Explore integrated scientific and behavi…

Microplastic pollution poses significant risks to food safety and public health across ASEAN countries. This research provides critical too…

723+ citations · h15
Nanotechnology Medication Safety
Novel Time Series Machine Learning Methodology for High-Dimensional Data
University of Strathclyde Dr Jiazhu Pan, Prof Ke Chen 🎓 Data Analysis 🎓 Econometrics Deadline: 05 Jun 2026

Funded PhD at the University of Strathclyde focused on new machine learning methods for imputation, forecasting, and anomaly detection in high-dimensional time series data.

Dynamic Behaviour and Engineering Optimisation of PEM Water Electrolysers under Real-World Conditions with AI Support
Monash University Malaysia Prof Meng Nan Chong, Dr Joshua Zheyan Soo, Dr Chin Vern Yeoh 🎓 Artificial Intelligence 🎓 Chemical Engineering Deadline: 31 Dec 2026

Funded PhD at Monash University Malaysia focused on PEM water electrolysers, combining engineering and AI to improve hydrogen production under real-world operating conditions.