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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.

These PhD opportunities are often developed with partners from several DTPs. Further details on each partnership can be found here:

  • ARIES (formerly EnvEast) (with the Universities of East Anglia, Essex, Kent, Plymouth and Royal Holloway University of London)
  • NERC GW4+ (with the Universities of Bristol, Bath, Exeter and Cardiff)
  • SWBio (with the Universities of Bristol, Bath, Cardiff and Exeter, alongside Rothamsted Research)
  • SuMMer (with Bangor University) CDTS303

New for this year:
Marine Research Plymouth (MRP) studentship opportunities

Four adverts have been launched as part of the MRP partnership between the University of Plymouth, Plymouth Marine Laboratory and the Marine Biological Association. Details of which can be found below.

New PhD opportunities will be listed on this page when they become available, to be kept informed of new opportunities you may wish to follow us on Twitter or Facebook.


ARIES (Advanced Research and Innovation in the Environmental Sciences) projects (PML hosted): 

Ocean Sulphur

Are sea ice and surface foam important in determining oceanic uptake of CO2?

The global oceans take up about a third of the carbon dioxide (CO2) we emit, and about half of this uptake occurs within the Southern Ocean surrounding Antarctica. This dampens global heating so understanding air-sea CO2 exchange in the Southern Ocean is essential for predicting future climate. While air-sea CO2 exchange through an unbroken water surface is reasonably well understood, how sea ice and whitecaps (formed via bubbles from wave breaking) affect CO2 exchange are very poorly known. Because the Southern Ocean is substantially covered by sea ice and/or whitecaps, our poor understanding of these processes limits the accuracy of current and future CO2 flux estimates.

Ocean Sulphur

Using novel techniques to understand biological controls on volatile sulfur in the ocean

The oceans release huge quantities of a gas called dimethylsulfide (DMS), which plays a key role in regulating Earth’s climate via the formation of atmospheric particles and clouds. DMS is produced when marine phytoplankton die and break apart, or by bacteria as they feed on the substances that phytoplankton excrete.
We know the products of DMS have an important influence on climate – their cooling effect is similar in size (but opposite in sign) to the global warming caused by human CO2 emissions. But gaps in our understanding remain, some of which relate to short term bloom events often associated with intense DMS production. Complex phytoplankton community dynamics are likely to be critical during such events, but models still do not include a suitable level of detail to capture this.


ARIES Projects (PML Co-hosted)

Bouncing back from macroalgal-dominated reefs: microbial and chemical effects of algal removal and coral transplanting.

Dr Miriam Reverter, University of Plymouth
Dr Mahasweta Saha, Plymouth Marine Laboratory
Dr Andy Foggo, University of Plymouth, School of Biological and Marine Sciences

Climate change is causing major ecological shifts, with important consequences for ecosystem functioning. In coral reefs, coral-algal transitions are the most well-known shift. Following algal increases, an array of mechanisms that reinforce algal dominance and prevent coral recovery are established. For example, algal chemicals can promote changes in microbial communities, negatively affecting corals. Algae can damage corals upon contact through the presence of chemical defences or by transferring opportunistic bacterial pathogens. To date, understanding of these mechanisms, their reversibility, and their effects on key reef functions remains limited. The objective of this project is to investigate the microbial and chemical mechanisms that reinforce algal-dominated reefs and to evaluate if algal removal and coral transplantation can reverse these mechanisms to help recover coral-dominated reefs.

Marine Research Plymouth projects
(PML co-supervising)


How do Organic Nutrients Sustain Shelf Seas Productivity?

Second Supervisor (External Lead): Dr Katherine Helliwell
Lead Supervisor (Director of Studies): Professor Mark Fitzsimons
Third Supervisor: Dr Andy Rees

Marine phytoplankton play vital roles in regulating the global climate, contributing almost half of net primary production. A major factor controlling phytoplankton assemblages is the availability of crucial nutrients including nitrogen (N) and phosphorus (P), the supply of which can vary dramatically in space and time. The aim of this project is to integrate field and laboratory methodologies to further examine organic N and P usage and coordination by phytoplankton in the Western English Channel.


Use of AI and computer vision to develop next generation marine biological observing capability

Lead Supervisor (Director of Studies): Professor Kerry Howell
Second Supervisor: Dr James Clark
Third Supervisor: Dr Pierre Hélaouët

Predicting how ocean life will respond to pressures from increasing human use and climate change is the basis for science-informed decision-making. It requires development of models that enable forecasting of possible outcomes in 'what if' scenarios. This studentship will investigate current Artificial Intelligence (AI) capability to deliver ecologically meaningful metrics from image-based data; and in so doing develop the methods and tools to support the wider application of AI to image-based biological observations.


Sound of Sharks: assessing the spatial and temporal distribution of sharks in Plymouth Sound and surrounding waters to inform sustainable ecosystem management approaches

Lead Supervisor (Director of Studies): Dr Emma Sheehan
Second Supervisor: Professor David Sims
Third Supervisor: Dr Peter Miller

Elasmobranchs (sharks and rays) play a key role in maintaining ecosystem structure and function, building ocean-human connections through tourism, and underpin valuable recreational and commercial fisheries, but elasmobranchs are globally threatened with extinction. By combining acoustic telemetry (tagging and tracking), video surveys and Earth observation systems, this PhD will build a better understanding of elasmobranch movement and distribution surrounding Plymouth Sound, providing insight into the effect of current management measures on elasmobranchs and facilitate the development of improved management plans and conservation measures.


New approaches to image dynamic sinking behaviour in marine phytoplankton

Second Supervisor (External Lead): Dr Glen Wheeler
Lead Supervisor (Director of Studies): Professor Alex Nimmo Smith
Third Supervisor: Dr James Clark

The rate at which phytoplankton cells sink to the deep ocean plays a critical role in the global carbon cycle. Phytoplankton cells can actively control their orientation and buoyancy, indicating that biological processes play a major role in determining carbon export, although the factors controlling sinking rates remain poorly understood. New technologies allowing direct imaging of sinking phytoplankton cells are providing novel insight, and this project aims to use these technologies to better understand the biological processes that influence sinking rates in a range of marine phytoplankton and hence determine global carbon fluxes.





The application deadline for these studentships is Tuesday 9 January 2024 at 2359 GMT. Interviews will take place from 22 February to 8 March 2024. For more information about the NERC GW4+ Doctoral Training Partnership please visit


Building a digital twin navigating autonomous underwater vehicles to monitor water quality

Lead Institution: Plymouth Marine Laboratory
Lead Supervisor: Jozef Skakala, Plymouth Marine Laboratory, Modelling group
Co-Supervisor: Prathyush P Menon, University of Exeter, Faculty of Environment, Science and Economy
Co-Supervisor: Juliane Wihsgott, Plymouth Marine Laboratory, Marine biogeochemistry group
Co-Supervisor: David Ford, Met Office, Exeter

We propose for the student to develop a proof-of-concept DT to demonstrate that multiple AUVs could be successfully navigated to investigate oxygen depletion/minima zones using their own measurements (of temperature, salinity, chlorophyll and oxygen itself) as well as Earth Observation (EO) and model data. The initial plan, which we are happy to later adapt to the student's initiatives, needs, and interests, is that the student will run virtual DT experiments using a state-of-the-art physics-biogeochemistry ocean model (NEMO-FABM-ERSEM), acting as the "real-world ocean" in which AUVs take "samples". Virtual EO data will be similarly extracted from the model outputs and perturbed by noise to represent observational uncertainty, providing an idealized framework in which the true ocean state and all model and observational errors are known, allowing the effectiveness of the developed ML/AI algorithms to be accurately assessed. Building on a recent work of Skakala et al (2023), the student will develop ML model to forecast DO values from the virtual glider measurements and EO data. The ML model will then be used together with path-planning algorithms, designed by the student, to optimize the trajectory and sampling strategy of multiple gliders. The work will demonstrate the feasibility of the DT AUVs control for tracking hypoxia and provide a framework for real-world applications. The work will further investigate/advise what variables need to be measured, as well as their locations and sampling resolution, for a successful future hypoxia tracking mission.

Find out more and apply


The Transport & Cycling of Terrigenous Carbon in UK and Falkland Island Coastal Waters

Lead Institution: Plymouth Marine Laboratory
Lead Supervisor: Andy Rees, Plymouth Marine Laboratory
Co-Supervisor: Dan Mayor, University of Exeter, Biosciences
Co-Supervisor: Chris Evans, UK Centre for Ecology and Hydrology, Bangor
Co-Supervisor: Paul Brickle, South Atlantic Environmental Research Institute
Co-Supervisor: Vas Kitidis, Plymouth Marine Laboratory

During this project, you will investigate processes that transform carbon transported between land and the ocean within several contrasting environments in order to better understand the fate and impact in receiving waters and overlying atmosphere. There will be opportunities to join existing research teams at the host organizations to address high-level questions related to carbon transport and transformation.

  1. What mechanisms control CO2 and CH4 release from an agriculturally dominated estuary (Tamar - SW England)
  2. How do the carbon characteristics differ between estuaries and coastal waters associated with drained and re-wetted peatlands (East Anglia)
  3. How does remineralization of peat deposited on the sea-bed impact the chemistry of benthic sediments and overlying waters (Falkland Islands)

Human activities on land have resulted in the elevated release of material between land and ocean, and this project will work alongside areas of impacted land and restoration attempts. Each of the areas highlighted above will require the student to work between rivers and the coastal zone to collect samples and deploy sophisticated analytical instrumentation to study dissolved and particulate carbon, dissolved oxygen, and methane and CO2 concentrations and fluxes.

Find out more and apply

Microscopic ocean life viewed through a new lens

Lead Institution: Plymouth Marine Laboratory (PML)
Lead Supervisor: Dr James Clark, PML, Marine Systems Modelling
Co-Supervisor: Dr Sareh Rowlands, University of Exeter, Computer Science
Co-Supervisor: Dr Sophie Pitois, Cefas, Ecology and Plankton Science
Co-Supervisor: Elaine Fileman, PML, Marine Ecology and Biodiversity
Co-Supervisor: Claire Widdicombe, PML, Marine Ecology and Biodiversity
Co-Supervisor: Robert Blackwell, Cefas, Data science and statistics

The project can be steered in several directions, and the successful student will have the opportunity to shape the project around their interests. Example research questions include the variability of plankton abundances on hourly-daily timescales, spatial scales governing patchiness in plankton communities, and plankton populations' responses to short- and long-term perturbations. The student may also decide to research how these new data types can be integrated into existing plankton survey and monitoring programs, which rely on more traditional measuring techniques.
The following gives an example of a set of objectives around which a project could be built:
i) Collect existing and new data on plankton abundances using the Pi-10 and IFCB cameras.
ii) Assess the efficacy of existing machine learning algorithms for classifying planktonic organisms within image data collected by the Pi-10 and IFCB cameras.
iii) Quantify and investigate sub-daily changes in plankton community composition at Station L4 in the Western English Channel using data from the IFCB and Pi-10 camera systems.
iv) Quantify and investigate short (~m) spatial scale changes in plankton community composition in Pi-10 image data collected aboard the RV Cefas Endeavour.
It is anticipated the student will work directly with the IFCB (PML) and Pi-10 (PML, Cefas) camera systems, and will take part in research cruises organised by both PML and Cefas. They will help to deploy, operate and service the three cameras; and use the data to study plankton communities. They will work with Machine Learning software to classify plankton within the images and use time series analysis and statistical approaches to tease apart patterns within the data and drivers of variability.

Find out more and apply

GW4+ co-supervising projects

Identifying the chemical signals needed to revive dormant bacteria

Lead Supervisor
Dr Sariqa Wagley, NERC Independant Research Fellow, University of Exeter

Additional Supervisors
Dr Eunice Lo, Research Fellow in Climate Change & Health, University of Bristol
Dr Mahasweta Saha, Senior Scientist, Plymouth Marine Laboratory

When bacteria encounter extreme or unfavourable environmental conditions, they enter a state of dormancy to protect themselves, however, they can re-awaken when favourable environmental conditions return. These dormant cells can act as a reservoir of disease in the environment and are a concern for human health. A major problem in the prevention of bacterial diseases is that dormant cells are not detectable by routine tests making them difficult to study. In this project, we will exploit new approaches to understand the mechanisms by which dormant bacterial Vibrio cells (our study organism) emerge as active disease-causing pathogens in the environment. The student will detect and quantify Vibrio species in their various functional states (active and dormant) from water, sediment, and shellfish sites from a coastal site in England and will require a background in microbiology. There will be opportunities to carry out field work and gain expertise in a broad range cutting-edge techniques including flow cytometry, cell sorting, and preparing samples for Mass spectrophotometry (LCMS) for studying chemical signals involved in bacterial dormancy.  This project will be flexible and will be developed together with Dr Sariqa Wagley depending on the strength and interests of the PhD student.  

Find out more and apply

The closing date for applications is 2359 hours GMT Tuesday 9 January 2024.


AI for ocean health: machine learning emulators for predicting key ocean health indicators under climate change

Lead Supervisor

Professor Chunbo Luo, University of Exeter

Additional Supervisors

Tinkle Chugh, University of Exeter, Computer Science

Xiaoyang Wang, University of Exeter, Computer Science 

Jozef Skakala, Plymouth Marine Laboratory, Marine ecosystem modelling

Lee de Mora, Plymouth Marine Laboratory, Marine Ecosystem Modelling

Location: Streatham Campus, University of Exeter, Exeter, Devon.

Climate change significantly affects the health of our oceans. Directly influenced ocean health indicators (OHIs) include net primary productivity, oxygen, and pH/acidity, with major impacts on food security, livelihoods and economy, as well as providing crucial feedback to climate. The most direct way to understand how these OHIs respond to the increase in carbon dioxide in the atmosphere and the warming planet is by running multi-decadal simulations (both future projections and hindcasts) of Earth System Models. However, such models are too complex and computationally prohibitive to explore large and complex landscapes of emission scenarios for ‘what-if’ analyses to inform policy and decision-making. With rapid developments in machine learning (ML) in the last decade there is an emerging research interest to develop ML emulators trained on complex model climate simulations that can accurately reproduce the results of complex marine ecosystem models with order of magnitude less computation. Such ML emulators could provide valuable tools for researchers, policy makers and environmental managers. The emulators could particularly benefit users from developing countries who lack access to high-performance computational resources and established data archives. This project will test the hypothesis that machine learning (ML) models can be used to accurately reproduce the results of sophisticated mechanistic marine ecosystem models at a fraction of the computational cost. Our aim is to create ML based emulators that can do the job of the mechanistic models faithfully (see Fig.1) and make these available to the research and policy communities as an open-source tool. More specifically, our central hypothesis is that ML models trained on the outputs of CMIP6 Earth system models will be able to reveal crucial patterns in the responses of net primary production, oxygen, and acidity to changes in atmospheric carbon dioxide concentrations, temperature, and other key physical variables. The student will be encouraged to discuss the detailed research directions, to better match the interests and maximise the project outcomes. 

Find out more and apply

The closing date for applications is 2359 hours GMT Tuesday 9 January 2024.

SuMMeR projects

Ocean Sulphur

CDTS303: Coastal Vigilance: Harnessing AI, Remote Sensing, and Citizen Science for Enhanced Observation and Monitoring of Land-to-Coastal Pollutant Transport

Lead Supervisor
Iestyn Woolway, University of Bangor

Additional Supervisors
Stefan Simis, Plymouth Marine Laboratory

Our world's coastal zones, brimming with vital ecosystems, face a pressing threat: the unrelenting transport of pollutants from land to sea. Dive into the cutting-edge realm of our PhD project, where we are at the forefront of change, marrying Artificial Intelligence (AI), remote sensing technologies, and the power of citizen science to revolutionise the way we observe and monitor the movement of pollutants. By harnessing these ground-breaking forces, we're on a mission to safeguard these delicate ecosystems, empower informed decision-making, and shape a more sustainable future.

Find out more and apply

Scenario Doctoral Training Partnerships

Treatment of model bias in Marine Ecology forecasting

Lead supervisor:
Alison Fowler from Uni of Reading/NCEO


David Ford - Met Office
Dr Jozef Skakala - PML
Prof Amos LawlessDr A Fowler.

Location: This studentship is a joint project with the Met Office and Plymouth Marine Laboratory (PML). The student will have the opportunity to spend time working at the Met Office and PML over the lifetime of the project. The student will also have the opportunity to attend ECMWF training courses on data assimilation and advanced training courses at Reading organised by the Data Assimilation Research Centre and the National Centre for Earth Observation

The monitoring and forecasting of marine ecology are essential to understanding the current and future health of our coastal oceans. Long-range forecasts allow for the assessment of the impact of climate change on ocean biomass and pH trends, while shorter time-scale forecasts can help predict sudden dangerous events, such as harmful algal blooms, or oxygen depletion. The forecasts are generated using sophisticated numerical models of marine biogeochemistry. To enable the numerical models to stay in line with the true underlying system a technique known as data assimilation (DA) is used to systematically blend the model with observations made from a myriad of instruments. One rich source of observations comes from satellite-derived sea surface chlorophyll concentrations (see figure), a proxy of how much life there is in the ocean. DA algorithms are based on mathematical principles that make approximations about the uncertainty in both the model and observations. One of the most fundamental approximations is that both are unbiased estimates of the true underlying system (i.e. they neither systematically over- nor under-estimate). Unfortunately, this can be far from true for marine ecology models due to the complex biogeochemical processes they need to represent [Fowler et al 2022]. As such, model biases are a significant limitation of the skill of marine ecology forecasts. Different approaches to treating bias within DA exist, but in order to apply these techniques an estimate of the bias or its statistics is needed. This is particularly challenging given that the biases are likely to have high variability in space and time. In order to tackle this challenge of marine ecology forecasting, you will collaborate with marine ecology and data assimilation experts from the Plymouth Marine Laboratory, the Met Office, and the National Centre for Earth Observation. You will utilise the latest advancements in machine learning (ML) in order to predict the model bias. Even with the most sophisticated ML techniques, it is anticipated that there will still be limitations on how good the estimate of the model bias is due to the uncertainties and incomplete coverage of the available observations that can be used to train the ML algorithms. For example, the satellite-derived sea surface chlorophyll concentration data can itself have large biases and few observations are available of the sub-surface or of key variables other than chlorophyll. The second aim of this project is therefore to develop DA techniques that can address model biases while being resilient to the inherent limitations of the estimated model biases. Once developed, these techniques will be applied to a real-world marine ecology forecasting system.

Find out more and apply

The closing date for applications is 2359 hours GMT Tuesday 9 January 2024.

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



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