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

Economics Network; Southampton; Hybrid; GBP 55,000 - 75,000; Full time

Benefits offered by this job

Personal development budget £6,000

Job summary

Compute Maritime Ltd seeks a Machine Learning Engineer to embed physics-informed AI tools into NeuralShipper, a marine vessel design platform, as part of a KTP with the University of Southampton. You will translate research into viable software, validate CFD-based models, and develop optimisation workflows for vessel performance across concepts to operations.

The role requires strong Python and ML/DL experience, plus CFD tools like STAR-CCM+.

Qualifications

• MSc/MEng or PhD (desirable) in Machine Learning, AI, CFD, Hydrodynamics, Optimisation, or a related field.
• Experience applying ML and DL to engineering or physical systems.
• Strong programming in Python; C++/MATLAB desirable.
• Experience with DL frameworks (PyTorch, TensorFlow, or JAX).
• Experience with CFD tools such as STAR-CCM+.
• Understanding naval architecture, ship hydrodynamics, or design analysis.
• Experience in physics-informed ML, surrogate modelling, generative AI or design optimisation is desirable.
• Entrepreneurial mindset with ability to build commercial acumen alongside technical strength.

Responsibilities

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

Skills

Python

C++

MATLAB

PyTorch

TensorFlow

JAX

CFD

Hydrodynamics

Naval architecture

Generative AI

Design optimisation

Education

MSc/MEng

PhD

Tools

STAR-CCM+

Job description

A Computational Ship Hydrodynamics and Design Optimisation specialistis 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 Ltdand the University of Southampton.

Find out more about Knowledge Transfer Partnerships here: https://www.ktp-uk.org/

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.

The Machine Learning Engineerwill be required to undertake the following:

• 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.

The successful Machine Learning Engineerwill have the following 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.

Further details:

• Job Description and Person Specification

As a university we aim to create an environment where everyone can thrive and are proactive in fostering a culture of inclusion, respect and equality of opportunity. We believe that we can only truly meet our objectives if we are reflective of society, so we are passionate about creating a working environment in which you are free to bring your whole self to work. With a generous holiday allowance as well as additional university closure days we are committed to supporting our staff and students and open to a flexible working approach.