PhD Study : Using Machine Learning to Predict Animal Diseases on Farms and Decrease Antimicrobial Resistance

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Summary

Data analysis is commonly used to predict the health and performance of livestock in agriculture – ‘smart-farming’ is attracting significant interest and commercial backing harnessing platforms based on the Internet of Things and Industry 4.0.  Developing novel and intelligent approaches to detect and predict animal health is now central to well-informed risk management plans, and in particular with respect to the use of veterinary medicines, including antibiotics. Antimicrobials, especially antibiotics, have saved millions of lives since their first discovery in 1940s. However, inappropriate use of this medicine has caused  the serious and growing threat of Antimicrobial Resistance (AMR) in both humans and animals – where the bugs are resistant to the drugs. AMR is one of the major threats to both human and animal health as well as food security, agriculture and economics.

Within agriculture, AMR can be linked  increased animal fatality and morbidity rate and therefore, it lowers the productivity of animal farms and increases the production costs. Consequently, AMR can affect the food security at national and international level. Bacteria which are resistant to drugs can be transmitted from animal to human by direct contact, through food or other means such as contaminated water or environment with AMR bacteria of animal origin.  Although farmers record medicines use, only high level antibiotic sales information is captured and as such it becomes difficult to mitigate the threat of AMR in agriculture. The European Food Safety Authority (EFSA) set up a new legislation, in 2013, on monitoring AMR in food-producing animals. The results of this new monitoring indicated that the resistance to common antimicrobials in most of the EU Member States for food-producing animals is more than 20% which is considered a high risk.

As a result, according to UK’s five-year national action plan, UK is set to reduce the antibiotic use in food-producing animals through livestock sectors up to 25% between 2016 and 2020 in order to “strengthen the stewardship for responsible use”. Modern technology has enabled some new applications to help farmers capture signs of animal disease (Meet Betty, the Artificial Intelligence Cow App) manage medicines (VetIMPRESS) and to assist vets in making better informed decisions about the health of animals. However, there is still an essential need to develop smart models that can help vets in predicting health related events prior to the animal disease diagnosis. This prediction can help in decision making and risk management and therefore, monitoring the antimicrobial usage at farm level and controlling AMR.

The PhD student will conduct research to develop new data-driven models using data processing and machine learning techniques to predict farm based animal diseases in advance, in order to enhance a better decision making system to reduce AMR.  They will be given the opportunity to work with the dataset provided by Agri-Food and Biosciences Institute (AFBI) in accordance with their animal health and welfare program with the outcomes of the project helping policy makers to develop risk management and decision making policies and schemes.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

  • Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 65%
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

  • Full Award: (full-time tuition fees + £19,000 (tbc))
  • Part Award: (full-time tuition fees + £9,500)
  • Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

The Doctoral College at Ulster University

Key dates

Submission deadline
Friday 7 February 2020
12:00AM

Interview Date
March 2020

Preferred student start date
September 2020

Applying

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