Description

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 use large language model for an automatic design of the tower. We target to develop an AI-driven assistant ("AI-Copilot") to automate the complex design and analysis process of the hybrid offshore wind tower structures.

We are recruiting a PDRA working on the royal society ISPF project. The project is funded through Royal Society under ISPF grant working with Brazil, and led by Dr Fangying Wang from the Department of Civil Engineering and Management, Faculty of Science and Engineering at The University of Manchester.

The successful candidate will contribute to the development of an AI-driven assistant (“AI-Copilot”) to automate the complex design and analysis processes of hybrid offshore wind tower structures. The role requires a combination of expertise in structural engineering, computational modelling, and artificial intelligence. This work aligns with the University of Manchester's commitment to cutting-edge research in engineering and sustainable development, as well as the broader goals of the Royal Society to support impactful scientific endeavors.

What you will get in return:

• Fantastic market leading Pension scheme
• Excellent employee health and wellbeing services including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure over the Christmas period
• Local and national discounts at a range of major retailers

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

Our University is positive about flexible working you can find out more here

Hybrid working arrangements may be considered.

Please be aware that due to the number of applications we unfortunately may not able to provide individual feedback on your application.

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Any recruitment enquiries from recruitment agencies should be directed to People.Recruitment@manchester.ac.uk.

Any CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Fangying Wang

Email: fangying.wang@manchester.ac.uk

General enquiries:

Email: People.recruitment@manchester.ac.uk

Technical support:

0161 850 2004 https://jobseekersupport.jobtrain.co.uk/support/home

This vacancy will close for applications at midnight on the closing date.

Please see the link below for the Further Particulars document which contains the person specification criteria.