Research Assistant (Fixed term, Part time)

Wind energy currently accounts for the largest global energy resources transition portion in reaction to climate change. Brazil has set targets to install 16GW of offshore wind power capacity by 2050, utilising abundant offshore wind resources, especially in its extensive coastline along the Atlantic Ocean. Unlike the UK, which has been the global leader in offshore wind energy since 2008, Brazil is now in the early stages of offshore wind energy development, urgently requiring advanced offshore wind turbine tower design optimised for its environmental conditions and local resources. 

The project will help Brazil achieve its 2050 target by offering design optimisation of an innovative hybrid steel-concrete system, i.e., concrete-filled double-skin tubular (CFDST) sections for offshore wind turbine towers. Compared to conventional steel towers, the efficient use of concrete and steel materials in CFDST offers superior mechanical properties and savings in cost. In the project, a combined testing, numerical modelling, and machine learning (ML) aided design approach will be adopted, utilising the respective expertise in Brazil (testing and wind tower design experience) and the UK (modelling and ML techniques). We need to understand the details of?how CFDST behaves under complex combined loadings, including the detailed interaction between components. 

We are recruiting one research assistant (RA) to work on the Royal Society ISPF project with a focus on the machine-learning optimisation aspect of this project.

The post is offered on a part time (14.5 hours per week), fixed term contract until 30th March 2027.

Requests for secondment from internal candidates may be considered on the basis that prior agreement has been sought from both your current line manager and the manager of your substantive post, if you are already undertaking a secondment role.