PhD studentships

Plymouth Marine Laboratory (PML) works with a number of University partners to train tomorrow’s leaders in Environmental Science. These collaborations, known as Doctoral Training Partnerships (DTPs), offer postgraduate studentships and training across the full range of multidisciplinary environments, helping to enrich the student experience.

Each DTP will create a strong and active community of students that are able – and encouraged – to integrate, work, and learn together. Students will receive in-depth, advanced research training, as well as training in the professional and transferable skills essential in today's economy.

PML is a multidisciplinary, internationally renowned, strategic marine research centre. We have a number of prestigious and exciting opportunities for outstanding students wishing to conduct PhD projects in our areas of research excellence: Earth Observation Science & Applications, Marine Systems Modelling, Marine Ecology and Biodiversity, Marine Biogeochemistry and Observations and Sea and Society.


PhD studentship with Marine Research Plymouth

Applications are invited for a 3.5-year PhD studentship. The studentship is due to start on 1 October 2025.

The closing date for applications is 12 noon on Monday 6 January 2025.

PhD

Understanding the response of marine ecosystems to ocean-based carbon dioxide removal

Second Supervisor (External Lead): Dr Glen Wheeler
Lead Supervisor (DoS): Dr George Littlejohn
Third Supervisor: Professor Helen Findlay

The development of ocean carbon dioxide removal (oCDR) technologies, aimed at helping oceans sequester atmospheric carbon dioxide to prevent excessive warming of our planet, have attracted substantial interest recently. Ocean alkalinity enhancement (OAE) is one such example, where the addition of finely ground mineral rocks to seawater causes carbon dioxide to be drawn down. The project will examine the impact of OAE on marine phytoplankton, testing the resilience of various species to episodes of low carbon dioxide, which will help us understand how, when and where oCDR technologies can be deployed.

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PhD

Sea to Sky: leveraging AUVs and satellites to determine floating wind impacts on Celtic Sea key ecosystem drivers

Second Supervisor (External Lead): Dr Lilian Lieber (also at UoP)
Lead Supervisor (DoS): Professor Alex Nimmo Smith
Third Supervisor: Dr Peter Miller

The rapid expansion of floating offshore wind (FLOW) infrastructure into deeper, seasonally stratified shelf seas like the Celtic Sea could have profound consequences for ocean dynamics through impacts on ocean fronts, and hence for key ecosystem drivers. Ocean fronts form at the interface of tidally well-mixed and seasonally stratified waters, providing biological hotspots. Despite their recognized importance, frontal habitats remain poorly studied and FLOW impacts are virtually unknown. This project will utilise autonomous underwater vehicles (AUVs) and high-resolution satellite remote sensing to understand FLOW interactions with ocean dynamics in the Celtic Sea, addressing the need for innovative monitoring approaches.

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PhD

AI-driven biodiversity insight: enhancing underwater ecosystem monitoring through advanced computer vision

Second Supervisor: Professor Kerry Howell (also at UoP)
Lead Supervisor (DoS): Dr Dena Bazazian
Third Supervisor: Dr Pierre Hélaouët
Fourth Supervisor: Dr David Moffat

The health of our oceans is critical to the planet’s overall environmental stability, yet marine biodiversity is under increasing threat from climate change, overfishing, and pollution. Traditional methods of monitoring underwater ecosystems are often limited by challenges such as difficult access and poor visibility. There is an urgent need for innovative approaches that can provide accurate, real-time biodiversity data. This project seeks to harness the power of Artificial Intelligence (AI) and advanced computer vision to transform monitoring of underwater ecosystems. Automating species identification and behaviour analysis will improve the quality and efficiency of biodiversity assessments, providing vital insights to support conservation and sustainable management of marine resources.

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ARIES PhD Studentship

ARIES

Novel Autonomous Techniques to Understand a Volatile Problem: Biological Controls on Seawater Sulfur

Hosted at Plymouth Marine Laboratory
Supervisors

Professor Thomas Bell, Plymouth Marine Laboratory – contact me
Professor Carol Robinson, ENV, UEA
Dr Frances Hopkins, Plymouth Marine Laboratory
Dr Claire Widdicombe, Plymouth Marine Laboratory

The oceans release huge quantities of the gas dimethylsulfide (DMS), which contributes to the formation and growth of atmospheric particles and clouds, reflecting solar radiation. DMS is a key influence on Earth’s climate, with an effect similar in size but opposite in sign to global warming from human CO2 emissions.

DMS in seawater is produced when phytoplankton die and break apart, or by bacteria as they feed on substances plankton excrete. Seawater DMS levels can vary dramatically over small spatial and temporal scales, often spiking during plankton blooms. However, previous technical capabilities and sampling campaigns have not captured these variations in detail or fully understood the dynamics leading to elevated DMS levels. Current models struggle to accurately reproduce DMS observations, and future predictions for plankton and DMS levels remain very uncertain.

Learn more on ARIES website


NERC GW4+ DTP PhD Studentship for September 2025

Entry (Ref: 5414)

PhD

Optimal Sensor Placement for Environmental Analysis

Supervisors

Lead Supervisor: David Walker, University of Exeter
Additional Supervisors:
Dr James Clark, Plymouth Marine Laboratory (PML)
Jacqueline Christmas, University of Exeter

Location

Streatham Campus, University of Exeter

The question of where to place environmental sensors is common to all branches of environmental science. Well-established techniques, including those using Gaussian Processes, are often applied to determine optimal placement strategies. A recent study explored the use of a new technique, Convolutional Gaussian Neural Processes (ConvGNPs), for determining optimal air temperature sensor placements in Antarctica (Andersson et al. 2023). This technique offers several advantages, including the ability to fuse multiple data streams, handle missing data effectively, and outperform traditional methodologies.

This project will investigate the application of ConvGNPs for optimal placement of various marine sensors and compare their performance with alternative methods. Applications could include optimizing the placement of wave buoys or new biogeochemical sensors (e.g., measuring pCO2 to determine air-sea flux of carbon dioxide).

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See what some of our past PhD students, now both Visiting Postdoctoral Fellows, did during their time at PML: