Research Associate for Machine Learning Modelling for Next-Level Fast Nonlinear Wave-Structure Interaction

The University of Manchester has a strong history of research into offshore renewable energy, with leading international activity in wave hydrodynamics and computational engineering. We are here concerned with accurate and highly efficient modelling of nonlinear wave-structure interaction for floating offshore platforms. Nonlinear wave effects can govern low-frequency motion, mooring loads, extreme response and fatigue, but are commonly simplified in routine design because conventional calculations are too slow for the large number of sea states that need to be considered. This 36-month component addresses nonlinear wave input, second-order drift forcing and higher-order platform and mooring response through analytical and computational hydrodynamics, BEM calculations and reduced-order modelling. Irregular and multidirectional wave fields will be considered, together with full quadratic transfer functions (QTFs), wave forces and platform motions. Machine-learning and other data-driven techniques may be used selectively where they provide additional efficiency or insight. The project is funded by the Engineering and Physical Sciences Research Council and led by the University of Manchester. It will be undertaken with academic collaborators, an aligned postgraduate researcher and industrial partners in offshore renewables and engineering software. The overall project aim is to produce fast, physically interpretable models that retain important nonlinear wave effects and identify the conditions in which they become important for design. The resulting models, software and datasets will be developed for research use and eventual integration into offshore engineering design tools. Purpose of the Job The successful candidate will develop and run analytical and computational models of nonlinear waves, floating-body hydrodynamics and wave-structure interaction. They will use BEM and related tools to calculate and interpret hydrodynamic coefficients, QTFs, wave forces and platform motions; develop efficient reduced-order models; integrate the resulting methods with an existing coupled floating-platform code; and verify and validate the models using suitable numerical, experimental and field data. They will publish the research, present at major conferences, contribute to postgraduate supervision or co-supervision, and work closely with academic collaborators and industrial partners. We are looking for an enthusiastic and motivated researcher with, or close to completing, a PhD in fluid mechanics, applied mathematics, ocean or offshore engineering, mechanical engineering, physics or a related discipline. Sound knowledge of the governing equations of fluid mechanics and of linear, second-order or nonlinear wave interaction with floating or moored bodies is essential. Applicants should also have strong analytical and scientific-programming skills, practical experience of computational model development, and an interest in applying machine-learning or data-driven methods to physical systems. Experience of QTFs, BEM software, reduced-order modelling, multidirectional waves, floating-platform or mooring dynamics, or higher-order hydrodynamics would be advantageous. The School of Engineering is strongly committed to equality, diversity and inclusion and holds an Athena Swan Bronze Award. We welcome applications from all sections of the community, and all appointments are made on merit. We are committed to supporting the development of research staff and to providing an inclusive, collaborative environment in which colleagues can build their skills and careers. 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 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 recruitmentservices.people@manchester.ac.uk Any CV’s submitted by a recruitment agency will be considered a gift. Enquiries about the vacancy, shortlisting and interviews: Name: Dr Tim Tang Email: tim.tang@manchester.ac.uk Name: Prof Peter Stansby Email: p.k.stansby@manchester.ac.uk General enquiries: Email: recruitmentservices.people@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.