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Funded PhD Opportunity

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

Subject: Engineering


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

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • 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

    The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:

    Vice Chancellors Research Studentship (VCRS)

    Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant 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.

    Vice-Chancellor’s Research Bursary (VCRB)

    Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant 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.

    Vice-Chancellor’s Research Fees Bursary (VCRFB)

    Fees only award (PhD fees + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees 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.

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. 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.

    Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies


Other information


The Doctoral College at Ulster University

Key dates

Submission deadline
Friday 7 February 2020

Interview Date
March 2020


Applying

Apply Online  


Campus

Jordanstown campus

Jordanstown campus
The largest of Ulster's campuses


Contact supervisor

Dr Pardis Biglarbeigi


Other supervisors

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