Machine Learning Engineer for Ship Design and Hydrodynamics (KTP Associate)
Develop physics-informed AI tools for ship design optimization in a collaborative KTP between a university and a deep-tech company.
Best for:Best for experienced machine learning engineers with a strong background in computational fluid dynamics, hydrodynamics, and physics-informed AI, seeking a hybrid academic-industry role with commercial acumen.
Overview
This position is a Knowledge Transfer Partnership (KTP) Associate role at the University of Southampton, working closely with Compute Maritime Ltd, a London-based deep-tech company specializing in AI-driven ship design. The successful candidate will integrate research into commercially viable solutions by embedding physics-informed generative AI tools into a marine vessel design platform. The role involves developing and validating fast predictive models for ship resistance, propulsion performance, and energy efficiency using computational fluid dynamics (CFD) and benchmark data. The candidate will also design and implement multidisciplinary optimization methods, extending NeuralShipper’s capabilities to wind-assisted propulsion and rigid sail systems. This hybrid role offers a unique blend of academic research and industry collaboration, with a focus on translating cutting-edge AI into practical applications for the maritime sector.
What you'll do
- Translate and embed research into commercially viable solutions by managing work packages for integrating AI tools into ship design platforms.
- 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 optimization methods, integrating them into NeuralShipper as scalable software tools for automated vessel design improvements.
- Extend NeuralShipper’s capabilities to wind-assisted propulsion and rigid sail systems, collaborating with industry stakeholders for validation.
- Manage a £6,000 personal development budget for training, conferences, and professional memberships.
Requirements
Degree required: MSc/MEng in Machine Learning, AI, Computational Fluid Dynamics, Hydrodynamics, Optimization, or a related discipline (PhD desirable but not mandatory)
Language: English (proficiency required for collaboration and documentation)
Instruction: English
- Experience applying machine learning and deep learning to engineering or physical systems.
- Strong scientific programming skills in Python.
- Understanding of naval architecture, ship hydrodynamics, or vessel performance.
- Experience with engineering simulation tools relevant to CFD, hydrodynamics, or vessel performance (e.g., STAR-CCM+).
- No explicit nationality or residency restrictions mentioned.
- No age or prior experience caps mentioned.
Funding & terms
How to apply
Deadline: 2026-10-26
Applicants must submit their CV and a cover letter through the provided external application portal. Ensure your application highlights relevant experience in machine learning, CFD, and ship hydrodynamics. The application process requires detailed documentation of technical skills and entrepreneurial attributes.
- CV
- Cover letter
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