AI-Driven Ship Design & Hydrodynamics Engineer

The Knowledge Transfer Network Limited; Greater London; Hybrid; GBP 60,000 - 90,000; Full time

Job summary

Compute Maritime Ltd, a London-based deep-tech company, seeks a Machine Learning Engineer to embed physics-informed generative AI tools within a marine vessel concept, generation and evaluation platform as part of a Knowledge Transfer Partnership (KTP) with the University of Southampton.

You'll translate research into commercially viable solutions, develop fast CFD-informed models predicting ship resistance, propulsion and energy efficiency; extend NeuralShipper for wind-assisted propulsion and

Qualifications

• Advanced degree in ML/CFD or related field.
• Experience applying ML to engineering or physical systems.
• Strong Python programming; C++ or MATLAB desirable.
• Familiarity with CFD tools and physics-informed modelling.

Responsibilities

• Translate research into commercially viable solutions by managing work packages.
• Develop and validate fast, physics-informed models for ship resistance, propulsion and energy efficiency using CFD and benchmark data.
• Design and implement multidisciplinary optimisation methods for NeuralShipper as scalable software tools.
• Extend NeuralShipper to wind-assisted propulsion and rigid sail systems with industry validation.

Skills

Python

C++

Deep Learning

Machine Learning

Physics-informed ML

CFD

Education

MSc/MEng or PhD in ML/CFD/Engineering

Tools

PyTorch

TensorFlow

JAX

STAR-CCM+

NumPy/SciPy

Job description

Compute Maritime Ltd, a London-based deep-tech company, seeks a Machine Learning Engineer to embed physics-informed generative AI tools within a marine vessel concept, generation and evaluation platform as part of a Knowledge Transfer Partnership (KTP) with the University of Southampton.

You'll translate research into commercially viable solutions, develop fast CFD-informed models predicting ship resistance, propulsion and energy efficiency; extend NeuralShipper for wind-assisted propulsion and