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

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.

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 NeuralShipper, 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.

Responsibilities:

- 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 NeuralShipper as robust and scalable software tools for automated vessel design improvement.
- Extend NeuralShipper's capabilities to wind-assisted propulsion and rigid sail systems, working with industry stakeholders to validate the tools against practical design requirements.

Required 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, TensorFlow, 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.