Postdoc in Maritime AI and Fuel-Efficient Vessel Operations at Delft University of Technology Delft University of Technology in Netherlands

Delft University of Technology (TU Delft) is seeking a Postdoctoral Researcher in Maritime AI and Fuel-Efficient Vessel Operations. The successful candidate will join the Maritime & Transport Technology department, working under the supervision of Associate Professor Andrea Coraddu. The research focuses on developing hybrid physics–AI models to predict vessel fuel consumption under real operational conditions, accounting for factors such as weather variability (waves, wind, currents) and progressive hull fouling.

This position offers the opportunity to collaborate with a strong ecosystem of academic and industrial partners, including MARIN (Maritime Research Institute Netherlands), Damen, RH Marine, SafeSystems B.V., and Spliethoff. The research aims to advance the state-of-the-art in maritime technology by integrating artificial intelligence with mechanical and maritime engineering principles to improve vessel efficiency and sustainability.

Applicants should hold a PhD in Mechanical Engineering, Computer Science, Maritime Engineering, or a related discipline. Experience with AI, machine learning, or hybrid physics–AI modeling is required, and familiarity with maritime operations and environmental modeling is highly desirable. Strong programming skills and the ability to work collaboratively in a multidisciplinary team are expected. Excellent English communication skills are essential.

The position is fully funded, with salary and benefits provided according to TU Delft standards. For more information and to apply, candidates should visit the official TU Delft careers page. The application process involves submitting a CV and cover letter through the online portal. For further inquiries, candidates may contact Associate Professor Andrea Coraddu.

Keywords: Maritime AI, Fuel-Efficient Vessel Operations, Hybrid Physics–AI Models, Mechanical Engineering, Maritime Technology, Vessel Fuel Consumption, Operational Conditions, Weather Variability, Hull Fouling.