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

University of Exeter - Department of Computer Science

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students
Funding amount: Tuition fees and stipend for 3.5 years (currently £15,609 p.a. for 2021/22)
Hours: Full Time
Placed On: 21st October 2021
Closes: 10th January 2022
Reference: 4239

Funding: Also entitled to a research budget of £11,000 for an international conference, lab, field and research expenses and a training budget of £3,250 for specialist training courses and expenses.

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

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

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 http://nercgw4plus.ac.uk/ 

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.

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

For information relating to the research project please contact the lead Supervisor Dr Anjan Dutta (webpage: http://emps.exeter.ac.uk/computer-science/staff/ad735) via email: A.Dutta@exeter.ac.uk 

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. More information is available on the University’s advert.

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