Postdoc Researcher in Maritime AI and Fuel-Efficient Vessel Operations

Postdoc Researcher in Maritime AI and Fuel-Efficient Vessel Operations

Join TU Delft and contribute to the transition towards cleaner and smarter shipping. As a postdoctoral researcher in Maritime AI, you will develop data-driven and physics-informed models to optimise fuel efficiency and reduce emissions in real-world vessel operations.

At TU Delft, you will contribute to research that addresses one of the most pressing challenges in the maritime sector: reducing fuel consumption and emissions while maintaining safe and efficient operations. Within this project, you will develop advanced modelling approaches that combine artificial intelligence with physical insights to better understand and predict vessel performance under real operating conditions such as variable weather and progressive hull fouling.

The position is embedded in the Maritime AI Innovation Lab, a collaborative initiative involving TU Delft, MARIN, Damen, RH Marine, Safe Systems, Spliethoff, and Ocean AI, and aims to translate cutting‑edge research into practical tools for cleaner and more efficient ship operations.

The maritime industry is undergoing a major transition towards more sustainable operations, driven by stricter environmental regulations and global decarbonisation targets. Digitalisation and AI play a key role in this transition, enabling more accurate predictions of fuel consumption, improved maintenance strategies, and optimised vessel performance. The long‑term goal of this research is to support the maritime sector’s transition to lower‑emission and more energy‑efficient operations by enabling reliable, data‑driven decision support for vessel performance monitoring, maintenance planning, and operational optimisation.

By combining domain knowledge with AI, the project contributes to the development of trustworthy digital tools that can be deployed in real‑world maritime settings and help accelerate decarbonisation across the industry.

In this role, you will develop and apply hybrid modelling approaches to improve the energy efficiency and operational performance of maritime vessels. As a postdoctoral researcher, you will:

• Develop hybrid models that combine physics‑based knowledge with AI techniques to predict vessel fuel consumption under operational conditions
• Design and validate data‑driven biofouling detection and monitoring methods, enabling condition‑based maintenance strategies
• Integrate environmental factors (waves, wind, currents) and operational data into predictive performance models
• Collaborate with partners to test and validate your methods on real‑world datasets and demonstrator platforms
• Contribute to scientific publications, conference presentations, and project deliverables
• Support the supervision of MSc and PhD students working in related areas

You will be part of a large team of PhD students working on different aspects of the energy transition in the maritime sector. You will work in a stimulating, interdisciplinary environment, bridging cutting‑edge research with industrial relevance. The position provides a unique opportunity to advance the state of the art in maritime digital twins, AI for energy efficiency, and sustainable shipping technologies.

We are looking for a motivated and capable postdoctoral researcher who combines independence with a collaborative mindset. You are proactive in your approach, able to engage effectively with stakeholders in both academia and industry, and willing to contribute to the supervision of MSc and PhD students within the group.

Furthermore, you meet the following criteria:

• You have expertise and/or a strong interest in maritime energy systems, data‑driven modelling, and hybrid physics–AI approaches.
• You hold a PhD (or are close to completion) in maritime engineering, mechanical engineering, applied mathematics, computer science, or a related field.
• You have a solid background in modelling of dynamic systems and/or machine learning for engineering applications.
• You have an interest in applying these methods to fuel consumption, propulsion, and biofouling monitoring in maritime contexts.
• Experience with data analysis, simulation…