Supervisors

Lead Supervisor

Dr James Clark, PML, Marine Systems Modelling
 

Additional Supervisors

Dr Sareh Rowlands, University of Exeter, Computer Science
Dr Sophie Pitois, Cefas, Ecology and Plankton Science
Elaine Fileman, PML, Marine Ecology and Biodiversity
Claire Widdicombe, PML, Marine Ecology and Biodiversity
Robert Blackwell, Cefas, Data science and statistics

Location: Plymouth Marine Laboratory and University of Exeter

About the Partnership

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology,  the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science.

For eligible successful applicants, the studentships comprises:

• An stipend for 3.5 years (currently £18,622 p.a. for 2023-24) in line with UK Research and Innovation rates
• Payment of university tuition fee;
• A research budget of £11,000 for an international conference, lab, field and research expenses;
• A training budget of £3,250 for specialist training courses and expenses

Project details

Project Enquiries: Dr James Clark, jcl@pml.ac.uk

Project Background

Automated observing platforms and advances in machine learning techniques are revolutionising the way we observe and study the Natural Environment. This is especially true of the marine environment, and the myriad of microscopic planktonic organisms that inhabit the Earth’s oceans and seas. Although they are often invisible to the naked eye, plankton support all forms of multi-cellular life in the ocean. They also play an important role in regulating water quality and Earth’s climate.
Plymouth Marine Laboratory (PML) and the Centre for Fisheries and Aquaculture Science (Cefas) are at the forefront of a global effort to automate the process by which different planktonic organisms are identified and enumerated using state of the art camera systems and machine learning software. PML were recently awarded funding to develop and deploy an automated, dual-camera system for imaging and enumerating plankton in the Western English Channel (WEC). The system is built around an Imaging Flow Cytobot (IFCB) and a Plankton Imager 10 (Pi-10). Meanwhile, Cefas have been using a ship-based Pi-10 camera system to image and classify plankton in shelf seas surrounding the UK. In this project, the successful candidate will have the opportunity to work with world leading experts in the fields of machine learning (University of Exeter); plankton taxonomy and classification; and plankton ecology. They will become one of the first people to work with these new datasets and to study new temporal and spatial patterns within them.

Project Aims and Methods

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: How do plankton abundances vary on hourly-daily timescales, and what are the drivers behind the observed variability? What are the dominant spatial scales governing patchiness in plankton communities? And how do plankton populations respond 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 programmes, which at the current time 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.

Candidate requirements

The project will suit an ambitious, passionate student with a strong background in data science and a keen interest in the marine environment. The student should have a strong first degree in marine science, or a numerate discipline (e.g. computer science, physics, mathematics). They should be comfortable working on both practical and theoretical problems. Good programming skills (e.g. in Python) are desired.

Project partners

The student will have the highly valuable experience of undertaking scientific research training in 3 world-leading research institutions: an independent research organisation and charity (PML), a research-intensive university (UoE) and a UK government marine research agency working at the research-policy interface (Cefas). The project draws on a wide breadth of expertise from all 3 partners.

Training

The student will receive training in using both the Pi-10 and IFCB camera systems that form part of APICS at PML, and the Pi-10 camera at Cefas. They will undertake fieldwork in the Western English Channel with PML and take part in a research cruise on board the RV Cefas Endeavour. The student will be mentored in the use of Machine Learning software for analysing plankton image datasets and receive training in data analysis techniques. The student will attend at least one national and one international research conference to present their findings.

Background reading and references

1) Schmidt, K., Birchill, A. J., Atkinson, A., Brewin, R. J. W., Clark, J. R. et al., 2020. Increasing picocyanobacteria success in shelf waters contributes to long-term food web degradation. Global Change Biology, 26: 5574– 5587.
2) Scott, J., Pitois, S., Close, H., Almeida, N., Culverhouse, P., Tilbury, J., Malin, G., & Irigoien, X. (2021). In situ automated imaging, using the Plankton Imager, captures temporal variations in mesozooplankton using the Celtic Sea as a case study. Journal of Plankton Research, 43(2), 300-313.
3) Kerr, T., Clark, J.R., Fileman, E.S., Widdicombe, C.E. and Pugeault, N., (2020). Collaborative deep learning models to handle class imbalance in FlowCam plankton imagery. Ieee Access, 8, pp.170013-170032.
4) Lombard, F., Boss, E. , Waite, A. , Vogt, M. , Uitz, J. , et al. (2019): Globally Consistent Quantitative Observations of Planktonic Ecosystems , Frontiers in Marine Science, 6 (196) .

Eligibility

NERC GW4+ DTP studentships are open to UK and Irish nationals who, if successful in their applications, will receive a full studentship including payment of university tuition fees at the home fees rate.

A limited number of full studentships are also available to international students which are defined as EU (excluding Irish nationals), EEA, Swiss and all other non-UK nationals.  For further details please see the NERC GW4+ website.

Those not meeting the nationality and residency requirements to be treated as a ‘home’ student may apply for a limited number of full studentships for international students. Although international students are usually charged a higher tuition fee rate than ‘home’ students, those international students offered a NERC GW4+ Doctoral Training Partnership full studentship starting in 2023 will only be charged the ‘home’ tuition fee rate (which will be covered by the studentship). 

International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD. More information on this is available from the universities you are applying to (contact details are provided in the project description that you are interested in.

The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.

Equality, Diversity and Inclusion

The University of Exeter is committed to promoting and supporting equality, diversity, and inclusion within our working environments and is at the heart of all our activities.  With over 27,000 students and 6,400 staff from 180 different countries we offer a diverse and engaging environment where our diversity is celebrated and valued as a major strength. 
We actively encourage applicants with varied experiences and backgrounds and from all sections of the community regardless of age, race, ethnicity, sexual orientation, gender, religion, or disability status.  We are committed to creating an inclusive culture where all members of our community are supported to thrive.
Whilst all applicants will be judged on merit alone, we particularly welcome applications from groups currently underrepresented within our postgraduate research student community.  Reasonable adjustments are available for interviews and workspaces.

Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

All applicants would need to meet our English language requirements by the start of the  project http://www.exeter.ac.uk/postgraduate/apply/english/.

How to apply

Apply now

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

• CV
• GW4+ DTP Personal Statement - https://tinyurl.com/yhzvmduc  Please upload your completed Personal Statement  using the link to the form.  You must use this Personal Statement form as part of the application process.  No other format for cover letter or personal statement will be accepted.
• Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English, please see the entry requirements for details.
• Two references

Reference information
You will be asked to submit two references as part of the application process.  If you are not able to upload  your reference documents with your application please ensure you provide details of your referees.  If you provide contact details of referees only, we will not expect receipt of references until after the shortlisting stage. Your referees should not be from the prospective supervisory team.

If you are shortlisted for interview, please ensure that your two academic referees email their references to the pgrapplicants@exeter.ac.uk, 7 days prior to the interview dates. Please note that we will not be contacting referees to request references, you must arrange for them to be submitted to us by the deadline.

References should be submitted by your referees to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

The closing date for applications is 2359 hours GMT Tuesday 9 January 2024. Interviews will be held between 22 February and 8 March 2024.  For more information about the NERC GW4+ DPT please visit https://nercgw4plus.ac.uk

If you have any general enquiries about the application process please email pgrapplicants@exeter.ac.uk  Project-specific queries should be directed to the lead supervisor.

Data Sharing
During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.

Summary