Machine Learning Engineer

University of Southampton; City of Westminster; On-site; GBP 45,000 - 65,000; Full time

Benefits offered by this job

£6,000 training budget

Job summary

The University of Southampton seeks a Machine Learning Engineer for a Knowledge Transfer Partnership with Compute Maritime Ltd. You will develop fast, physics-informed ML models for predicting ship resistance and propulsion, and integrate them into NeuralShipper for automated vessel design improvement.

Working with industry partners, you will extend capabilities to wind-assisted propulsion, validate tools against practical design requirements, and contribute to commercial outcomes while

Qualifications

• MSc/MEng or PhD in ML, AI, CFD, hydrodynamics, optimisation, or related discipline.
• Experience applying ML and deep learning to engineering or physical systems.
• Strong Python programming; knowledge of C++ and MATLAB is desirable.
• Familiarity with PyTorch, TensorFlow, or JAX and CFD tools like STAR-CCM+.
• Understanding naval architecture and ship hydrodynamics is a plus.

Responsibilities

• Translate and embed research into commercially viable solutions via work packages.
• Develop fast, physics-informed ML models for ship resistance and propulsion.
• Design multidisciplinary optimisation methods and integrate into NeuralShipper.
• Extend capabilities to wind-assisted propulsion and validate with stakeholders.
• Collaborate with industry partners to meet practical design requirements.

Skills

Machine Learning

Deep Learning

Scientific programming (Python)

C++

MATLAB

Education

MSc/MEng or PhD in ML, AI, CFD, hydrodynamics or related discipline

Tools

Python

C++

MATLAB

STAR-CCM+

PyTorch

TensorFlow

JAX

Job description

This will be part of a Knowledge Transfer Partnership (KTP), which is a collaborative project between Compute Maritime Ltd and 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.

• 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 Engineer will 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.