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.
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