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Animal health in marine ecosystems: machine learning and data science to discover structure in unstructured data

  • Full or part time
  • Application Deadline
    Sunday, March 03, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

A great deal of data has been collected on marine animal health in the UK, including wild marine mammals and farmed salmon, but this material often exists in formats that are not amenable to large-scale analysis. These materials represent a wealth of untapped data for conducting population-based investigations of marine animal health.

Computing science has led to advances in our ability to search material in many formats, including natural-language documents. Recent development of “deep learning” and related techniques are advancing the automated extraction of context-specific information.
We are seeking a student to develop and apply approaches for deducing the structure of data from unstructured or loosely structured documents, and for augmenting this with data from other sources (e.g., photographs or digital images), to generate useful, context-specific knowledge regarding the health and welfare of marine animals.

The experimental approach will involve the application of machine learning, text mining/natural language processing (NLP), signal/image processing, and artificial intelligence to address problems in context detection and automated data extraction. The suitability of the approaches developed will be evaluated based on their ability to provide useful information from veterinary records for the investigation of spatial and temporal trends in disease and mortality in farmed salmon and in wild marine mammals.

Specific objectives of this project are 1) to develop and apply methods for the transformation of existing, relatively inaccessible archives and formats into data sets suitable for larger-scale analysis; 2) to explore automated methods for augmenting written data with material from other sources, such as photographs or digital images; and 3) to evaluate these methods by applying them to existing loosely structured data resources to identify spatial and temporal trends in marine animal health.

Applicants should have a Bachelor’s or master’s degree in computing science, data science, mathematics, or a closely related field, or a track record of research or work history in such a field in combination with a post-secondary degree.
The student will be located at Stirling and the SRUC Epidemiology Research Unit in Inverness and registered with the University of Stirling in the Division of Computing Science and Mathematics and starting on 1st October 2019.

Funding Notes

The stipend will be set at UKRI recommended levels for a 3.5 year-period and the studentship is funded to pay domestic tuition fee levels for UK/EU students. The student will receive an annual student stipend of £14,777 (£15,009 in 2019/20).This studentship will fund to pay the tuition fees at home fees rate only. International students must provide evidence of sufficient funds to cover the higher international student tuition fee level (approximately £16,740 per year would be required).

How good is research at SRUC - Scotland’s Rural College in Agriculture, Veterinary and Food Science?
(joint submission with University of Edinburgh)

FTE Category A staff submitted: 57.37

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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