Navigating the Future: AI-Driven Optimization of Maritime Logistics and Risk Forecasting

The maritime sector is undergoing a digital revolution, where AI-driven navigation and risk forecasting can optimize offshore logistics, improve safety, and enhance operational efficiency. However, current maritime navigation systems rely on static models with limited adaptability to real-time changes. This PhD project seeks to answer the following research question: How can AI-driven forecasting, route optimization, and risk assessment enhance the safety and efficiency of offshore maritime navigation?

To address this, the project will develop novel AI methodologies that integrate real-time ocean data, predictive analytics, and route optimization algorithms. The research will focus on three core areas: AI-driven forecasting, route optimization, and maritime risk assessment.

The project will leverage machine learning techniques such as Long Short-Term Memory (LSTM) networks, transformers, and ensemble learning models to enhance the accuracy of ocean condition forecasting. These AI models will be trained using historical buoy data, meteorological records, and real-time ocean observations. The study will also explore decision-support algorithms like A* pathfinding and minimum spanning trees to create an adaptive and efficient maritime navigation system.

Additionally, a risk quantification framework will be developed using SWAN model calibration and probabilistic uncertainty assessments to evaluate the reliability of AI-driven forecasts. The project will include collaboration with NeuWave, an industry leader in AI-enhanced maritime navigation, ensuring direct applicability of research outputs to real-world vessel routing and offshore operations.

This research aims to deliver a next-generation AI-powered decision-making tool for maritime logistics, helping to reduce operational costs, enhance safety, and support the growing offshore renewable energy sector. The findings will contribute to academic advancements in AI-based geospatial analytics while providing a commercial edge to maritime industries adapting to digital transformation. The PhD will culminate in a validated AI platform integrated within NeuWave’s system, setting a new standard for intelligent maritime navigation and risk assessment.

Candidates wishing to apply should complete the University of Liverpool application form [How to apply for a PhD – University of Liverpool] applying for a PhD in **Aerospace / Civil / Materials / Mechanical** Engineering and uploading: Degree Certificates & Transcripts, an up-to-date CV, a covering letter/personal statement and two academic references.