Machine Learning Engineer – Ship Design & Hydrodynamics (KTP Associate)

at University of Southampton, Southampton, United Kingdom

Listed on EduAtlas: live school vacancies from schools’ own careers pages and public job services. Apply on jobs.ac.uk.

Salary

£41,064 to £45,064 a year

Closing date

16 November 2026

Start

Not given in the advert

Type of post

IT and technicians
A support post, not a teaching post.

Contract

Full Time, Fixed-Term/Contract

First seen

29 September 2026, on jobs.ac.uk

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The advert

A Computational Ship Hydrodynamics and Design Optimisation specialist is required to work on an ambitious and novel project to embed physics informed generative AI tools within a marine vessel concept, generation and evaluation platform. This will be part of a Knowledge Transfer Partnership (KTP), which is a collaborative project between Compute Maritime Ltd and the University of Southampton. Find out more about Knowledge Transfer Partnerships here: https://www.ktp-uk.org/ Compute Maritime Ltd is a London-based deep-tech company bringing intelligence to the core of the global shipbuilding industry through generative artificial intelligence (AI) and high-performance computing. Through its proprietary technologies, most notably Neural Shipper, the company is building the first AI-native maritime design ecosystem, offering end-to-end solutions across the vessel lifecycle, from early concept design to operational optimisation. The Machine Learning Engineer will be required to undertake the following: Translate and embed research into commercially viable solution by managing a series of work packages. Develop and validate fast, physics-informed models for predicting ship resistance, propulsion performance and energy efficiency using CFD and benchmark data. Design and implement multidisciplinary optimisation methods, integrating them into Neural Shipper as robust and scalable software tools for automated vessel design improvement. Extend Neural Shipper’s capabilities to wind-assisted propulsion and rigid sail systems, working with industry stakeholders to validate the tools against practical design requirements. The successful Machine Learning Engineer will have the following skills, experience and attributes: MSc/MEng or PhD (desirable) in Machine Learning, AI, Computational Fluid Dynamics, Hydrodynamics, Optimisation, or a related discipline. Experience of applying machine learning and deep learning to engineering or physical systems. Strong scientific programming skills in Python , with experience in C++, MATLAB, or similar languages desirable . Experience with a deep learning framework such as PyTorch, Tensor Flow, or JAX (desirable). Experience with engineering simulation tools relevant to CFD, hydrodynamics, or vessel performance, such as STAR-CCM+. Understanding of naval architecture, ship hydrodynamics, vessel performance, or design analysis. Experience in physics-informed machine learning, surrogate modelling, generative AI, or design optimisation would be desirable. An entrepreneurial mindset and a willingness to build commercial acumen alongside technical strengths. Personal development : A separate £6,000 budget is available over the duration of the KTP for relevant training, conferences and professional memberships. Further details: Job Description and Person Specification As a university we aim to create an environment where everyone can thrive and are proactive in fostering a culture of inclusion, respect and equality of opportunity. We beli

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