PhD Studentship: Multi-Objective Generative Model × Reinforcement Learning based autopilot for cleaner maritime navigation

DoS: Dr Adán López-Santander (adan.lopez-santander@plymouth.ac.uk)

2^nd Supervisor: Dr Jahir Rizvi (mdjahir.rizvi@plymouth.ac.uk)

3^rd Supervisor: Professor Jonathan Lawry ( J.Lawry@bristol.ac.uk)

Applications are invited for a 3.5-year PhD studentship.

The studentship will start on 1^st October 2025.

Project Description

The maritime industry faces ambitious international targets to reduce emissions, driven by both legislative mandates and economic incentives. Companies that improve emissions performance gain competitive advantages, such as market access and reduced costs from taxes or penalties. Consequently, there is substantial interest within the research community in developing practical solutions to demonstrably reduce emissions in maritime operations.

One prominent area of research is propulsion technology, including alternative fuels and electric propulsion systems. While these technologies may offer significant emission reductions, their adoption faces considerable barriers, such as high fleet investment costs and the need for extensive infrastructure.

However, substantial emission reductions can be achieved through low-cost, infrastructure-independent measures —namely, optimizing how ships are driven, regardless of their propulsion technology. This proposed research focuses on developing intelligent autopilot systems capable of real-time optimization of ship states —rudder position, power settings, and ballast— to align with specific route, operational, and weather conditions. This could provide very rapidly an efficient, cost-effective, and impactful solution for reducing maritime emissions.

The project will explore the application of generative models combined with reinforcement learning to enhance autopilot systems in maritime navigation. The primary goal is to optimize real-time vessel sate during navigation, reducing emissions while maintaining high levels of operational efficiency.

The research will align with and gain valuable insights from the University of Plymouth’s Maritime Simulation Laboratory’s broader maritime-focused studies (https://www.plymouth.ac.uk/facilities/maritime-simulation-laboratory .

The successful candidate will become an integral part of the laboratory’s research team, contributing to and benefiting from its expertise and ongoing projects.

Eligibility

Applicants should have a first or upper second class honours degree in an appropriate subject and preferably a relevant Masters qualification. Applications from both UK and overseas students are welcome.

The studentship is supported for 3.5 years and includes full Home tuition fees, Bench fee plus a stipend of £20,780 per annum 2025/26 rate.  The studentship will only fully fund those applicants who are eligible for Home fees with relevant qualifications.  Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates.  The international component of the fee may be waived for outstanding international applicants.

There is no additional funding available to cover NHS Immigration Health Surcharge (IHS) costs, visa costs, flights etc.

NB: The studentship is supported for 3.5 years of the four-year registration period. The subsequent 6 months of registration is a self-funded ‘writing-up’ period.

If you wish to discuss this project further informally, please contact

Dr. Adán López-Santander at adan.lopez-santander@plymouth.ac.uk

To apply for this position please click the Apply button above.

Please clearly state the name of the studentship that you are applying for on your personal statement.

Please see here for a list of supporting documents to upload with your application.

For more information on the admissions process generally, please contact research.degree.admissions@plymouth.ac.uk

The closing date for applications is 12 noon on 28^th March 2025.

Shortlisted candidates will be invited for interview shortly thereafter.

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