University of Exeter funding: NERC GW4+ DTP PhD studentship

Machine Learning for Automatic Plankton Detection, Identification and Quantification. PhD in Computer Science (NERC GW4 + DTP) Ref: 3668

About the award


Lead Supervisor 

Dr Nicolas Pugeault, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter

Additional Supervisors

Dr James Clark, Plymouth Marine Laboratory

Mrs Elaine Fileman, Plymouth Marine Laboratory

Mrs Claire Widdicombe, Plymouth Marine Laboratory

Location: University of Exeter, Streatham Campus, Exeter EX4 4QJ

This project is one of a number that are in competition for funding from the NERC GW4+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the GW4 Alliance of research-intensive universities: the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five unique and prestigious Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology & Hydrology, the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in the Earth, Environmental and Life sciences, designed to train tomorrow’s leaders in scientific research, business, technology and policy-making. For further details about the programme please see

For eligible successful applicants, the studentships comprises:

  • An stipend for 3.5 years (currently £15,009 p.a. for 2019/20) in line with UK Research and Innovation rates
  • Payment of university tuition fees;
  • 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.
  • Travel and accomodation is covered for all compulsory DTP cohort events.
  • No course fees for courses run by the DTP

We are currently advertising projects for a total of 10 studentships at the University of Exeter


Students who are resident in EU countries are eligible for the full award on the same basis as UK residents.  Applicants resident outside of the EU (classed as International for tuition fee purposes) are not eligible for DTP funding. Residency rules are complex and if you have not been resident in the UK or EU for the 3 years prior to the start of the studentship, please apply and we will check eligibility upon shortlisting.

Project Background

Enumeration and classification of plankton from environmental samples is key to determining ecosystem function and their role in the marine food web. Traditional enumeration methods using microscopy are often slow and laborious. The availability of automated imaging microscopy platforms such as FlowCam has revolutionized the way plankton can be detected within their natural environment such that there is now a requirement for automated classification techniques to deal with the enormous volume of digital data produced. The aim of this project is to use state-of-the-art machine learning and pattern recognition approaches, such as deep learning, to analyse plankton shapes and develop automated algorithms for plankton identification and quantification. The project is a collaboration between the University of Exeter’s Institute for Data Science and AI and the Plymouth Marine Laboratory (PML), a charity undertaking pioneering marine research to further our understanding of the dynamic and complex marine environment and inform knowledge-based solutions to the challenges our oceans face.

Project Aims and Methods

The aims of this project will be as follows:

  • Explore the applicability and scalability of deep learning approaches for plankton identification from Flowcam imaging. The quantity of plankton species as well as the variety of appearances within each species makes this problem an especially challenging one for machine learning.
  • Develop an approach for large-scale plankton identification and quantification from microscopy images containing multiple targets.
  • Develop a comprehensive dataset and benchmark for plankton classification for the research community. 

The project will make use of state-of-the-art machine learning approaches, including Deep Neural Networks and Generative Adversarial Models to analyse the microscopy images. Note that this project offers a large flexibility in its aims and methods used and that both partners would expect the doctoral student to take the lead and propose further development as the project progresses and new developments arise in the literature.   


PML boat used for sample collection


Plankton images using FlowCam microscopy

Candidate Requirements 

The ideal candidate should have a first-class undergraduate degree, preferably in Computer Science or Mathematics, and optionally an MSc in Data Science, AI or a related subject. The candidate should have a broad interest in life science and into learning more about marine biology. 

CASE or Collaborative Partner 

PML has approximately 45 PhD or MRes Students and is a member of the GW4+ Doctoral Training Partnership (DTP).  PML provides access to exceptional capabilities in the collection and processing of marine samples, and quantitative analysis techniques. The student will be fully integrated into the relevant research groups at PML, where they will have the opportunity to learn how the image data is collected and used. As part of their training, they will have the opportunity to take part in at least one research cruise aboard the Plymouth Quest, which is owned and operated by PML. 


  • The student will benefit from all training resources available at the university of Exeter and the Plymouth Marine Laboratory.
  • The student will be offered to enrol in any course that could further her or his understanding of the required methodologies (eg, Machine Learning or Data Science). 
  • Attendance to the British Machine Vision Summer School will also be encouraged. 

References / Background reading list 

  • Ellen, J. S., Graff, C. A. and Ohman, M. D. (2019), “Improving plankton image classification using context metadata”, Limnology and Oceanography: Methods.
  • Wang, C., Zheng, X., Guo, C., Yu, Z., Yu, J., Zheng, H. and Zheng, B. (2018), “Transferred parallel convolutional neural network for large imbalanced plankton database classification”, in 2018 OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO), IEEE, pp. 1–5.
  • Lin, T.-Y., Goyal, P., Girshick, R., He, K. and Dollar, P. (2017), “Focal loss for dense object detection”, in Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988.
  • Orenstein, E. C. and Beijbom, O. (2017), “Transfer learning and deep feature extraction for planktonic image data sets”, in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 1082–1088.

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

How to apply

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
  • Letter of application outlining your academic interests, prior research experience and reasons for wishing to undertake the project.
  • 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.

Reference information
You will be asked to name 2 referees as part of the application process, however 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, 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 1600 hours GMT Monday 6 January 2020.  Interviews will be held between 10 and 21 February 2020.  For more information about the NERC GW4+ DPT please visit

If you have any general enquiries about the application process please email  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.


Application deadline:6th January 2020
Value:£15,009 per annum for 2019-20
Duration of award:per year
Contact: PGR Enquiries