Research Fellow (Maritime Safety and Data Analytics )

Job Description

The project will be engaging a Research Fellow to undertake advanced research on AIS-driven multi-vessel interaction analysis, with a particular focus on estimating navigational capacity in restricted and congested waterways (e.g., port approaches, straits, and narrow channels). Leveraging large-scale Automatic Identification System (AIS) datasets, the study will systematically extract vessel trajectories, characterize interaction patterns among heterogeneous vessel types, and identify critical factors influencing traffic flow efficiency and safety. It will further integrate multi-source data, including high-resolution meteorological and oceanographic information (e.g., wind, waves, currents, and visibility), to capture the impact of environmental conditions on vessel behavior and waterway capacity. Advanced data processing pipelines will be developed to handle massive, noisy, and heterogeneous datasets, ensuring data quality, scalability, and computational efficiency.

The selected RF will be required to collaborate closely with project team members on a range of activities:

1. Large AIS data process
2. AIS data based maritime safety assessment
3. Capacity estimation for the restricted waters
4. Preparation of technical reports and slides
5. Engagement with stakeholders through meetings and discussions.

Job Requirements

• Possess a PhD in Maritime Safety, Computer Engineering/Science, Data Science related subjects (incl. PhD to be received in 6 months after starting work).
• Experience in developing AIS data-driven models.
• Ability to sophisticatedly use at least one programming language, e.g., Python, R Java, SQL, Julia, C# and C++.
• Excellent interpersonal and communication skills.
• Values teamwork and display great leadership. Able to self-manage and self- motivated, with minimum supervision.
• Open to fixed-term contract.

Prioritized Requirements:

• AIS Data mining and analysis, machine learning skills.
• Research experience on maritime risk assessment and big AIS data processing.
• Good publication record in international leading journals.

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