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As Maritime Autonomous Surface Ships (MASS) advance toward higher levels of autonomy, their ability to navigate reliably in complex, uncertain, and data-limited environments becomes increasingly important. Real-world maritime operations expose sensors to harsh weather, dynamic sea states, interference, and degradation, all of which introduce uncertainty into navigation systems. Because autonomous vessels depend heavily on multi-sensor data fusion, ensuring robust, low-latency performance under fluctuating conditions is essential for safe and confident decision-making.

This research aims to develop a resilient, adaptive sensor-fusion methodology that enhances autonomous maritime navigation in the presence of degraded data, uncertainty, and intermittent sensor performance. The study focuses on understanding how multi-physics sensor inputs behave under real operational constraints and on designing fusion strategies that maintain reliability even when data quality fluctuates.

The International Maritime Organisation’s (IMO) target to reduce greenhouse gas emissions by 50% by 2050 has led to the emergence of Green Shipping Corridors (GSC) as a critical strategy for the maritime sector’s net-zero transition. The objective of this doctoral project is to construct a data-driven decision support framework, thereby translating the GSC concept into actionable implementation priorities. The integration of stakeholder commitments and the analysis of global network data are pivotal in addressing two core challenges: namely, the screening of priority candidate shipping routes and the planning of coordinated bunkering infrastructure. In contradistinction to methodologies that depend on predefined routes, this framework provides a transparent, scalable process that is applicable on a global scale. The final deliverables will comprise a structured portfolio of candidate green shipping routes alongside strategic infrastructure recommendations. These recommendations will empower governmental and industry stakeholders to develop efficient roadmaps and conduct collaborative investment assessments.

The effects of waste noise from shipping in ports and harbours, and the effects on the marine environment.

Forward osmosis is a method of membrane desalination in which fresh water is produced by the passage of water across a membrane along the osmotic gradient. This is achieved by placement of a semi-permeable membrane between the feed solution to be desalinated and a higher concentration draw solution. An osmotic gradient occurs across the membrane and causes water to pass from high to low water potential into the draw solution. Various methods can then be used to separate this new mixture into clean water and a renewed draw solution.
As current widely used desalination methods involve the use of high pressures (reverse osmosis) or temperatures (distillation processes), this low pressure, low temperature process could increase the energy efficiency of desalination both onboard ships as well as on shore. Forward osmosis also has advantages over these other methods in that it can also be used for wastewater treatment.
The efficiency of a forward osmosis plant strongly depends upon the design of both the membrane and the draw solution. These determine the speed and quality of freshwater production, as well as the startup and maintenance costs of the plant. Membranes must also be safe to use in drinking water and cost-effective to produce.
This project aims to develop new designs for membranes and draw solutions intended specifically for use in forward osmosis plants, in order to reduce the amount of energy required to produce potable water from sea water.

I previously studied mechanical engineering, and I completed my undergraduate degree at Liverpool John Moores in July 2025. From then I have joined the N0MES CDT as part of their second cohort. My current research focuses on the coupled Hydrodynamic and aerodynamic effects of floating offshore wind turbines (FOWT). When placed from distances around 15km to 100km from the shore they suffer from strong wake flows as the ambient air turbulence is less than onshore. Consequently, the wake persists for longer distances behind the rotor. Safe spacing of wind turbines on a wind farm requires reliable estimation of the wake shadow of the upstream and neighbouring turbines. These effects coupled with the hydrodynamic effects of the waves on the platform of each turbine create a difficulty predicting the performance of these farms. I will study these effects by using numerical and experimental techniques for the reliable estimation of the unsteady vortex shedding and collision that occurs in the wake region of FOWTs.

Dredged sediments are often disposed of offshore, which can lead to long-term sediment depletion along the coastline. Once deposited offshore, these sediments are challenging to resuspend and transport back inshore. This issue has prompted efforts to recycle dredged material. While this method can enhance habitat resilience, some studies suggest it is not optimal for dredging efficiency or coastal restoration efforts.
This project will examine case studies across the UK co-decided with the external partner to assess where dredged sediments could support coastal restoration efforts. Initially, we will identify existing coastal risks at each site. This will include the development of monitoring and digital twin components allowing for incorporating real-time data into modelling techniques. Subsequently, we will employ computer modelling, remote sensing, and machine learning techniques to predict future coastal risks under changing climate conditions. Finally, these tools will be used to test various sediment recycling methods, aiming to enhance both sediment budgets and the resilience of the overall coastal system. The project will also include a final life-cycle carbon budget analysis to evaluate the advantages and disadvantages of these innovative approaches compared to traditional coastal protection strategies.

Maritime fires pose critical risks to net-zero energy distribution and environmental protection, especially with the growing presence of lithium-ion battery (LIB) systems in electric vehicles transported on roll-on/roll-off (ro-ro) cargo decks. This PhD project will develop quantitative methods and data to (1) characterize fire size via heat release rate (HRR) for LIB-powered EVs—from single-pack ignition to multi-vehicle propagation in enclosed ro-ro environments; (2) assess EV-fire-induced structural failure mechanisms within ro-ro cargo spaces; and (3) quantify water pollution arising from active firefighting, including contaminated runoff, drainage measures, and potential marine discharge pathways.
The research will combine literature synthesis, bench- to intermediate-scale HRR measurements, scaling analysis to vehicle and multi-vehicle scenarios, and scenario-based modelling of fire spread in ro-ro decks. Structural response will be investigated through thermo-mechanical analysis calibrated against representative fire curves derived from measured/estimated HRR histories. Environmental impact work will quantify pollutant loads in firefighting effluents (e.g., dissolved metals, fluorinated species where relevant, and combustion by-products), evaluate drainage/containment strategies, and outline operational guidance to minimize marine pollution.
Expected outcomes include validated HRR datasets for LIB EV fires in ro-ro contexts; engineering tools for predicting fire spread and structural performance; and evidence-based recommendations for firefighting tactics, effluent containment, and environmental protection. The project aligns with N0MES themes on Energy distribution and Environmental impact and will be delivered in collaboration with Artec Fire and regional stakeholders, ensuring strong pathways to adoption in maritime operations and regulation.

Originally, I come from an economics background, with a particular interest in the mathematical descriptions of human behaviour from an environmental perspective. Since then, I have completed a masters at the University of Liverpool, which culminated in research examining the relationship between air particulate composition, deprivation and respiratory health across Liverpool using machine learning. Now, I am within the N0MES CDT, looking at AI-driven oceanic wave modelling with a focus on machine learning led site similarity for automated SWAN model implementation. During my time at the CDT, I am partnered closely with NeuWave Technologies, a company with expertise in oceanography and deep learning applications for offshore wind. This partnership helps to bridge the gap between academia and industry, and I am excited to make a meaningful impact in this field.

The maritime sector is a major contributor to global greenhouse gas emissions, and ports are central to both the challenge and the solution. As international trade and shipping volumes are projected to increase significantly by 2050, the environmental impact of ports will intensify unless effective measures are implemented. While many ports are beginning to adopt renewable energy technologies (RETs), there is a lack of comprehensive, comparative research on which technologies. There is a particular research gap regarding wind and solar, which are most suitable for port environments in terms of efficiency, sustainability, and compatibility with local biodiversity.

This project focuses on the Northwest region, where ports are uniquely positioned to harness wind and solar energy. However, the optimal approach for balancing energy generation with environmental stewardship and regional development remains unclear. Multi-disciplinary collaboration is essential to address these complex challenges, yet policy frameworks to support such collaboration are underdeveloped.

This research will systematically evaluate wind and solar RETs for port applications, with a focus on developing policy frameworks that enable effective collaboration between stakeholders from engineering, environmental science, policy, and industry. The project aims to identify which technologies and collaborative approaches are most effective for reducing emissions, supporting biodiversity, and advancing the UK government’s net zero targets.

By providing evidence-based recommendations, this work will guide policy and investment decisions, ensuring that ports in the Northwest can transition to sustainable energy in a way that benefits both the environment and the regional economy. The outcomes will help position the region as a leader in sustainable maritime energy development and contribute to broader climate change mitigation efforts.

Marine plastic pollution poses a significant threat to the ecosystem, creating an urgent need to address the problem. Traditional methods of mitigating oceanic microplastic pollution, such as cleanup or prevention, face immense challenges due to the scale of the issue and the difficulty of removing microplastics from water systems once they disperse. One promising alternative solution is to develop a direct conversion method that can transform these microplastics into fuel species, which can then be used as marine transport fuel.

Low-temperature plasma technology, which can activate gases and compounds as radicals, ions, etc., offers a promising route to break stable C-C/C-N bonds in common plastics using intermittent renewable energy. However, an efficient combination of cold plasma and catalysts needs to be developed. Therefore, this project aims to rationally develop catalysts for the plasma conversion of marine plastic into transport fuels, suitable for marine and offshore energy use. This project provides a novel solution to the marine microplastic issue by converting microplastics into sustainable transportation fuels through plasma-based technology. This approach addresses two major concerns: ocean pollution and the demand for cleaner fuel alternatives.

The number of marine structures for renewable energy is planned to dramatically increase in the next few years to deliver on the national and international commitments to implement the energy transition towards low-carbon energy production. However, these structures have a variety of negative and positive impacts on the marine environment, including the development of communities on the pristine hard substratum that is introduced in the ocean with wind turbines, which can offer new colonisable space to several species.

It has long been claimed that the marine structures could be colonised by non-native species, and that the creation of new wind farms (often in clusters) could create a series of stepping stones that could favourite the proliferation of invasive species, including the expansion of the invaded areas from coastal waters to offshore. While the presence of invasive species has been confirmed on marine structures (turbine foundations, scour protection), it is not really clear if the wind farms exacerbate the problem and if the ‘stepping stones’ dynamic is an actual risk.

The PhD will aim at better characterising this knowledge gaps through:

• A detailed literature analysis
• An analysis of correlation between invasive species and abiotic factors (e.g. tidal regime, depth, temp or salinity)
• An analysis of field data, with the analysis of the number of invasive species and a comparison between natural and artificial reefs
• The analysis of newly installed structures to understand whether invasive species are more prevalent during the first colonisation waves or in more mature communities; or
• Modelling the possible stepping stones effects based on the present and forecasted offshore developments map, possibly considering case studies and identifying species that pose particularly high risk

My PhD focusses on the impacts of offshore wind farms on breeding seabirds in Scotland. Specifically, it will investigate the differences in how individuals respond to the presence of offshore wind farms in their environment by analysing GPS data from bird-borne trackers. By comparing the differing capacities of individuals to behaviourally buffer this sudden environmental change, and then scaling up these responses to population-level outcomes, my research should help to better inform assessment and licensing for offshore wind farms. Before moving to Liverpool, I earned an Honours degree in Biology and Geography from the University of St Andrews, then interned and volunteered internationally for a year as a R&A International Scholar, investigating how technology and big data are being used in different conservation contexts. Having developed a keen interest in applied science through these experiences, the opportunity to work with Marine Directorate Scotland as my project partner is a highlight of being in the N0MES CDT.

Although there are a lot of initiatives, monitoring systems and scientific research in place for outdoor air quality in the UK, there is little understanding of the links between the nature of port cities, the demography, the behaviour and the health status of citizens, the type, location, material, interior of buildings, and indoor air quality. The Office for Health Improvement and Disparities (OHID) figures showed that air pollution was responsible for 5% of the deaths of people in Wirral aged over 30 in 2022. This PhD project aims to examine the correlation between measured indoor air quality indicators and reported health outcomes among different population groups including vulnerable people. This project has 3 key components: Studying the situation of pollutants, deeper analysis of indoor air quality and a study on human behaviours through qualitative research and digital engagement. This project will ultimately be able to inform the scientific community with a novel understanding of the subject of indoor air quality for a port city.

Air pollution has significant impact on human health, increasing the risk of heart and respiratory diseases, as well as lung cancer and strokes. Yet WHO data show that 99% the global population breathe in polluted air. The need to identify sources of pollution is even more pressing in port cities, where higher and more diverse ranges of outdoor air pollution have been reported due to ship emissions, cargo handling activities, land-based cargo traffic, and surrounding industrial zones.

This project aims to address the problem of identifying pollution sources in port cities by harnessing the power of cutting-edge machine learning / data analytics techniques and untapped data sources, allowing local authorities and public health bodies to target regulations and interventions to protect public health. It will also investigate the impact of net zero strategies generally associated with future port cities, such as green shipping, renewable energy and regeneration, on outdoor air pollution and its sources.

As autonomous shipping continues to evolve, maritime situational awareness becomes paramount for safe and efficient navigation in complex and dynamic environments. This project focuses on developing advanced situational awareness systems for autonomous vessels by integrating machine learning, historical data analysis, and real-time data processing to enhance decision-making capabilities. By analysing interactions between ships, environmental conditions, and navigational risks, the system aims to accurately predict potential hazards and vessel movements. This will enable autonomous ships to make informed, proactive decisions. Additionally, this research will address compliance with collision regulations (COLREGs) and develop intelligent algorithms for collision avoidance in complex waterways. To verify its effectiveness, the system will undergo testing with historical maritime incidents and real-world traffic scenarios, ensuring its practical application in improving maritime safety, operational efficiency, and the overall reliability of autonomous shipping technologies.

While fully autonomous vessels remain far from widespread adoption, recent advancements in maritime technology strongly indicate that autonomous shipping will shape the industry’s future. As Maritime Autonomous Surface Ships (MASS) increasingly incorporate advanced technologies, the challenge of effectively integrating human expertise into their development and operation has gained significant attention from researchers. This study explores the synergy between human competencies and autonomous technologies, aiming to bridge the gap between seafarer expertise and automation. technologies, aiming to bridge the gap between seafarer expertise and automation. Specifically, it identifies the competency gaps that seafarers face in adapting to advanced technologies aboard autonomous ships and proposes targeted training recommendations to support future workforce development.

The aim of this project is to develop a predictive modelling framework to assess the impact of extreme rainfall on nuclear power stations. While significant progress has been made in coastal flood risk assessment, freshwater flooding and erosion risks remain underexplored. This project will use advanced modelling techniques to evaluate how extreme rainfall affects nuclear infrastructure by simulating runoff, erosion, and sediment transport. The findings will generate probability maps of at-risk structures and evolving landscapes. By integrating UK Climate Change Projections, the study will provide a long-term risk assessment, supporting the development of climate-resilient strategies for the sustainable management of nuclear power stations.

The aim of this project is to develop an understanding of the coastal, estuarine and fluvial hazards that are presented along the Cumbrian coast, between Heysham and Carlisle. This research will highlight the connections between climatic change and increased risk to rail networks in the region, that are strategically important for energy transport. The initial objective is to produce a wider understanding of the research area, and the key coastal, fluvial and estuarine risks that are apparent.

The primary focus will be on setting up a regional coastal model on DELFT-3D, using wind, wave, and tide data to simulate SLR and coastal flooding in the future. The safe and efficient transport of energy materials is crucial in the UK’s goal of reaching net-zero. Therefore, it is vital that the risks posed to this stretch of track, because of climate change, are understood. By producing a regional model, the identification of vulnerable areas will produce foundations for further study. Any problem areas that are highlighted by the model, can be investigated in greater detail. This methodology also allows the research to be malleable and change direction depending on initial results and the needs of Network Rail.

Among the various sources of clean energy, hydrogen is considered as an ideal candidate as a clean fuel to carry and convert extra energy from intermittent renewable energy sources. While the electrolysis of pure water using noble metal catalysts is a well-established technology, its scalability for large-scale hydrogen production remains economically unfeasible. This necessitates the development of innovative, low-cost, and durable solutions for sustainable hydrogen generation, especially from seawater – a widely available and underutilized resource. This research will focus on developing durable, low-cost electrocatalysts tailored for seawater splitting. Using advanced additive manufacturing techniques, these catalysts are engineered for enhanced performance and longevity in saline environments, ensuring a practical approach to harnessing marine resources.

This project looks at the dynamics of mixed sand and gravel barriers in the English Channel, particularly evidence preserved in the geological record from the seafloor and aims to provide evidence of gravel beach response to accelerated sea level rise to inform the future response of barrier beaches that front energy infrastructure and aims to understand the past dynamics of the English Channel drowned barriers during historic sea level rise. To achieve these aims, the project employs a combination of data and laboratory work. The data sets are seismic profiles, which use sound waves to distinguish rock types as well as evidence of past morphological changes, and bathymetry (marine topography) which is a dataset showing the distribution of marine landforms for a particular area of the seabed.

The research will examine the role of autonomy in achieving safe, fuel-efficient ship operations, and in particular the effective combination of machine and human domains in busy, hazardous port environments. The research aims to establish robust functional allocation for effective human-machine operation of autonomous vessels to assure vessel and crew safety, and to achieve optimal routing and manoeuvring to reduce energy use and prevent economic losses. The research approach will test the robustness of autonomous systems in dynamic and uncertain port environments, and will develop the use of cases that demonstrate verification and validation, incorporate human values and ethics, and meet regulatory requirements.

The aim of this project is to model the process of the flocculation and settling process of the cohesive sediment plume generated from the seabed due to offshore installations. A two-phase sediment transport model with a coupled flocculation model will be used to consider the floc size, density, settling velocity and suspension concentration evolution under coastal hydrodynamics, including the influences of wave and current. This research will contribute to the understanding of sediment-pollutant feedback mechanisms in coastal areas while developing open-source computational tools for sustainable marine infrastructure planning.

This project focuses on the development and optimisation, through design-by-testing, of an innovative modular steel platform that can adapt to rising water levels. The innovative system can be implemented as new solution to protect key components of energy-related critical infrastructure/facilities, especially for production and storage in the maritime sector. Such new technology will guarantee enhanced structural performance and minimise losses due to failure and/or collapse. The research aims to analyse, re-design and perform experimental tests, both in structural laboratories and in-situ, of steel modular frames that are being used as resilient structural systems for critical energy-related facilities in coastal regions.

The research will assess the carbon credentials of shipbuilding and repair and aims to fully understand and quantify the shipyard’s carbon emissions across its entire operations. The purpose is for the shipyard to recognize where the largest uses of carbon are across both shipbuilding and repair activities, and to identify where changes in practice or interventions can be made to make substantial reductions. The research will form the cornerstone of future green upgrades to the shipyard and will therefore have a direct impact on the maritime sector’s ability to achieve its Maritime 2050 targets.