{"id":2170002,"url":"https://alion.io/job/southampton-machine-learning-engineer-ship-design-hydrodynamics-ktp-associate-2","title":"Machine Learning Engineer – Ship Design & Hydrodynamics (KTP Associate)","company":{"id":18662,"name":"Southampton","domain":"southampton.ac.uk","url":"https://alion.io/company/southampton","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_payroll_markers","remote_working_hours":{"label":"UK time","utc_offset_min":0,"utc_offset_max":0},"hiring_geo_confidence":"inferred","locations":["Southampton, United Kingdom"],"countries":["GB"],"hiring_countries":["GB"],"hiring_countries_total":1,"salary":{"min":41064,"max":45064,"currency":"GBP","period":"year","gross":null,"usd_annual":59556},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"C++","optional":true},{"name":"JAX","optional":true},{"name":"MATLAB","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"PyTorch C++","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true}],"status":"live","first_seen_at":"2026-09-29T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-09-29T00:00:00Z","board_verified":false,"closed_at":null,"days_open":20,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":20},"description":"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. \n\nThis will be part of a Knowledge Transfer Partnership (KTP), which is a collaborative project between Compute Maritime Ltd and the University of Southampton. \n\nFind out more about Knowledge Transfer Partnerships here: https://www.ktp-uk.org/\n\nCompute 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.\n\nThrough 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.\n\nThe Machine Learning Engineer will be required to undertake the following:\n\nTranslate and embed research into commercially viable solution by managing a series of work packages.\n\nDevelop and validate fast, physics-informed models for predicting ship resistance, propulsion performance and energy efficiency using CFD and benchmark data.\n\nDesign and implement multidisciplinary optimisation methods, integrating them into NeuralShipper as robust and scalable software tools for automated vessel design improvement.\n\nExtend NeuralShipper’s capabilities to wind-assisted propulsion and rigid sail systems, working with industry stakeholders to validate the tools against practical design requirements.\n\nThe successful Machine Learning Engineer will have the following skills, experience and attributes:\n\nMSc/MEng or PhD (desirable) in Machine Learning, AI, Computational Fluid Dynamics, Hydrodynamics, Optimisation, or a related discipline.\n\nExperience of applying machine learning and deep learning to engineering or physical systems.\n\nStrong scientific programming skills in Python, with experience in C++, MATLAB, or similar languages desirable.\n\nExperience with a deep learning framework such as PyTorch, TensorFlow, or JAX (desirable).\n\nExperience with engineering simulation tools relevant to CFD, hydrodynamics, or vessel performance, such as STAR-CCM+.\n\nUnderstanding of naval architecture, ship hydrodynamics, vessel performance, or design analysis.\n\nExperience in physics-informed machine learning, surrogate modelling, generative AI, or design optimisation would be desirable.\n\nAn entrepreneurial mindset and a willingness to build commercial acumen alongside technical strengths.\n\nPersonal development: A separate £6,000 budget is available over the duration of the KTP for relevant training, conferences and professional memberships.\n\nFurther details:\n\nJob Description and Person Specification\n\nAs 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.\n\nApply by 11.59 pm UK time on the closing date. 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