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Award details

Machine Learning with Fewer Labels for Automatic Plankton Classification, NERC GW4+ DTP PhD studentship for 2022 Entry, PhD in Computer Science Ref: 4239

About the award


Lead Supervisor

Dr Anjan Dutta, University of Exeter, DepaExeertment of Computer Science

Additional Supervisors

Dr James Clark, Plymouth Marine Laboratory

Elaine Fileman, Plymouth Marine Laboratory

Claire Widdicombe, Plymouth Marine Laboratory

Dr Nicolas Pugeault, University of Glasgow, School of Computing

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

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 further details about the programme please see

For eligible successful applicants, the studentships comprises:

  • An stipend for 3.5 years (currently £15,609 p.a. for 2021/22) 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


Setup at PML including Plymouth Quest

Project Background

Marine and freshwater plankton are a physiologically and morphologically diverse group of organisms that inhabit aquatic environments around the world. Many plankton are microscopic and are invisible to the naked eye. However, in oceans and bodies of freshwater their population sizes can grow to levels that allow them to be easily viewed from space. In the ocean, microscopic photosynthetic plankton perform a similar role to terrestrial plants, trapping energy from the sun and using it to form organic material. In turn, these organisms are grazed by different types of microscopic zooplankton that feed heterotrophically, and ultimately support the growth of organisms higher up the food chain including fish.

To build an understanding of how marine planktonic communities are structured, how they function and how they change in time, it is important to be able to monitor the abundance of different planktonic organisms. As the manual identification and quantification of plankton is both time consuming and costly, a significant amount of effort has gone into developing automatic imaging and classification systems using cutting edge machine learning techniques [1, 2]. However, to date these techniques have relied on the existence of large libraries of labelled images, which themselves are difficult to create and rely on the work of expert taxonomists [3]. If a high accuracy, low-shot classification system for the automatic recognition of plankton images could be developed, it would have significant advantages over existing technologies.

With the further development of deep learning, computer vision has made great progress, mainly due to the powerful feature extraction capabilities of Convolutional Neural Networks (CNN) and the creation of large-scale data sets used for training, such as ImageNet [4]. However, a CNN’s capability to recognize objects is greatly reduced if only a small training sample size is available. For humans, only a tiny number of samples are required for the successful identification of objects [5]. To make machines also able to recognise objects with a small training sample size, the field of low-shot learning has been gradually and continuously developing [6,9,10].Dutta_2

Example image from ZooScan dataset [2]

Project Aims and Methods

At present, most algorithms for classifying marine plankton images depend on the existence of numerous training samples, and mainly CNN and transfer learning methods are adopted to train image classifiers [1,2]. The aim of this project is to investigate the effectiveness of low-shot machine learning techniques for the automatic classification of plankton image data when only limited training data is available. Specifically, we will focus on two different types of learning paradigms: (1) Few-shot and (2) Zero-shot:

1. Few-shot learning is designed to adapt quickly to new categories from few examples. The methods work by employing techniques for model initialization, metric learning, data enhancement, and transfer learning. In this project, we will focus on meta-learning-based models [6], which prevent the model from overfitting by extracting transferable knowledge from a set of tasks, resulting in a more generalized model.

2. Zero-shot learning is a particular problem setup in machine learning, where at test time, a learner observes samples from classes that were not observed during training and needs to predict the class they belong to [10]. We will consider the attribute annotations of plankton and attention based deep learning models [10] for mapping visual information from plankton images to the attribute space where the prediction of images from unseen classes can be done during the inference time.

The project will require the student to employ exciting and innovative techniques from the cutting edge of computer vision research to the important problem of plankton classification and enumeration. They will evaluate the accuracy of different classification algorithms; contrast them with current state of the art approaches; and investigate approaches for maximising performance with a view to operational deployment. The student will work with automatically acquired plankton image data that has been collected over multiple years by expert taxonomists at Plymouth Marine Laboratory (PML). To ensure the student has a solid understanding of how the image data is collected and the various scientific questions it is used to address, the student will be encouraged to take part in sampling work at sea aboard the Plymouth Quest, and to assist with laboratory-based classification in PML’s laboratories.


Planktons from PML collections

Candidate requirements

The project will suit a student with a degree in a numerate discipline (e.g., computer science, physics, mathematics) who has a strong background in computing, and in particular Python programming. A good working experience with deep learning frameworks, such as PyTorch or TensorFlow, is desirable.

Project partners 

The student will have the exciting opportunity to work collaboratively with the University of Exeter, Plymouth Marine Laboratory, and the University of Glasgow. The student will be based in the University of Exeter and will have ample opportunities to visit other supervisors at their corresponding institutes.


The student will have the opportunity to join a strong, established interdisciplinary team with expertise in computer vision, machine learning, plankton taxonomy and marine ecology. They will develop and gain experience in using highly desirable skills, including the development and application of cutting-edge machine learning techniques. The student will present their findings at national and international venues.

Background reading and references

[1] T. Kerr, J. Clark, E. S. Fileman, C. E. Widdicombe and N. Pugeault, Collaborative Deep Learning Models to Handle Class Imbalance in FlowCam Plankton Imagery. IEEE Access, 2020.

[2] Picheral et al., EcoTaxa: a tool for the taxonomic classification of images,, 2017.

[3] Faillettaz et al., Imperfect automatic image classification successfully describes plankton distribution patterns. Methods in Oceanography, 2016.

[4] Deng et al., ImageNet: A Large-Scale Hierarchical Image Database. CVPR, 2009.

[5] Lake et al., Human-level concept learning through probabilistic program induction. Science, 2015.

[6] J. Snell, K. Swersky, and R. S. Zemel, Prototypical Networks for Few-shot Learning. NeurIPS, 2017.

[7] Lee et al., Plankton classification on imbalanced large-scale database via CNN with transfer learning. ICIP, 2016.

[8] S.-M. Schröder, R. Kiko, J.-O. Irisson, R. Koch, Low-Shot learning of plankton categories. GCPR, 2018.

[9] F. Alamri and A. Dutta, Implicit and Explicit Attention for Zero-Shot Learning. GCPR, 2021.

Useful links

For information relating to the research project please contact the lead Supervisor Dr Anjan Dutta (webpage: via email:


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 2022 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.

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, 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, 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 Friday 10 January 2022. Interviews will be held between 28 February and 4 March 2022.  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:10th January 2022
Value:£15,609 per annum for 2021-2022
Duration of award:per year
Contact: PGR Enquiries