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

The Methodist Church in Britain; London Borough of Southwark; Hybrid; GBP 35,000 - 55,000; Full time

Benefits offered by this job

£6,000 personal development budget

Hybrid working may be available

Job summary

Compute Maritime Ltd in collaboration with the University of Southampton is seeking a Machine Learning Engineer for a Knowledge Transfer Partnership to embed physics-informed AI into NeuralShipper, a vessel design platform. You will build fast ML models for ship resistance and propulsion, and extend to wind-assisted propulsion and rigid sails.

You will work with industry stakeholders and use CFD/benchmark data to validate tools, developing scalable software solutions for commercial deployment.

Qualifications

• Experience applying ML/DL to engineering or physical systems.
• Experience with engineering simulation tools such as STAR-CCM+ for CFD, hydrodynamics or vessel performance.

Responsibilities

• Manage work packages to turn research into commercially viable solutions.
• Develop and validate fast physics-informed models for ship resistance, propulsion and energy efficiency using CFD and benchmarks.
• Design and implement multidisciplinary optimisation methods and integrate them into NeuralShipper as scalable software tools.
• Extend NeuralShipper to wind-assisted propulsion and rigid sail systems.
• Collaborate with industry stakeholders to validate tools against practical design requirements.

Skills

Python

Machine learning

Deep learning

CFD

Education

MSc/MEng/PhD in related discipline

Tools

STAR-CCM+

PyTorch

TensorFlow

JAX

Job description

Machine Learning Engineer post on a Knowledge Transfer Partnership between Compute Maritime Ltd, a London deep-tech firm, and the University of Southampton. The work centres on embedding physics-informed AI into NeuralShipper, a vessel design platform, including fast models for ship resistance and propulsion and automated optimisation tools. It stands out for extending the platform to wind-assisted propulsion and rigid sail systems, validated with industry stakeholders. Python skills and experience with simulation tools such as STAR-CCM+ are essential.

What the job involves

• Manage a series of work packages to turn research into commercially viable solutions
• Develop and validate fast, physics-informed models predicting ship resistance, propulsion performance and energy efficiency, using CFD and benchmark data
• Design and implement multidisciplinary optimisation methods and integrate them into NeuralShipper as robust, scalable software tools
• Extend NeuralShipper to wind-assisted propulsion and rigid sail systems
• Work with industry stakeholders to validate the tools against practical design requirements

Experience

• Applying machine learning and deep learning to engineering or physical systems
• Engineering simulation tools such as STAR-CCM+ for CFD, hydrodynamics or vessel performance

Skills & knowledge

• Strong scientific programming in Python
• Understanding of naval architecture, ship hydrodynamics, vessel performance or design analysis

Other

• Entrepreneurial mindset and willingness to build commercial acumen

Nice to have

• MSc, MEng or PhD in machine learning, AI, CFD, hydrodynamics, optimisation or a related discipline
• C++, MATLAB or similar languages
• Deep learning frameworks such as PyTorch, TensorFlow or JAX
• Physics-informed machine learning, surrogate modelling, generative AI or design optimisation experience

What’s offered

• £6,000 personal development budget over the KTP for training, conferences and professional memberships
• Hybrid working may be available