Machine Learning Engineer – Ship Design & Hydrodynamics (KTP Associate)
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Job Description
This KTP Associate role seeks a Machine Learning Engineer to embed physics-informed generative AI tools into a marine vessel design platform. Responsibilities include developing and validating physics-informed models for ship performance and implementing optimization methods for automated design. Essential qualifications include an MSc/MEng or PhD in a relevant field, experience applying ML to engineering systems, and strong Python programming skills.
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Key Responsibilities
- Develop and validate fast, physics-informed models for predicting ship resistance, propulsion performance and energy efficiency using CFD and benchmark data.
- Personal development : A separate £6,000 budget is available over the duration of the KTP for relevant training, conferences and professional memberships.
Requirements
- 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.
- The Machine Learning Engineer will be required to undertake the following:
- MSc/MEng or PhD (desirable) in Machine Learning, AI, Computational Fluid Dynamics, Hydrodynamics, Optimisation, or a related discipline.
About This Position
Where is this position based?
The position is based at University of Southampton, in GB. Check the job description above for flexible or hybrid arrangements.
How do I apply for this job?
Click the Apply button on this page — it takes you to University of Southampton's official application portal, where you submit your application directly to the university. Tenurify only aggregates the listing and never handles applications or charges candidates.
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