• University of Manchester Featured PhD Programmes
  • University of Surrey Featured PhD Programmes
  • University of Exeter Featured PhD Programmes
  • University of Stirling Featured PhD Programmes
  • University of Birmingham Featured PhD Programmes
  • Northumbria University Featured PhD Programmes
  • University of Macau Featured PhD Programmes
University of Warwick Featured PhD Programmes
Imperial College London Featured PhD Programmes
Anglia Ruskin University Featured PhD Programmes
University of Southampton Featured PhD Programmes
University of Strathclyde Featured PhD Programmes

Applications of machine learning to precision potato blackleg prediction

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr P Skelsey
    Dr S Humphris
    Dr I Hein
    Prof G Hughes
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Do you want your research to make a difference to society?

We are offering a unique opportunity to contribute to global food security and have an immediate impact on food production in Great Britain. The project incorporates the challenges of improving our understanding of the epidemiology of a devastating crop pathogen, and providing new tools and methods for managing the disease that will be implemented at a national scale in GB.

Why is this project important?

Potato is the third most important food crop in the world after rice and wheat in terms of human consumption. More than a billion people worldwide eat potato, and global total production exceeds 300 million metric tons. Pests and diseases directly threaten global food security, causing estimated losses of up to 40% in the annual production of potatoes. Among the most destructive of plant pathogens are the bacteria that cause blackleg and soft rot of potatoes. Several approaches have been studied to control blackleg, but the degree of success has been variable. This is because the processes underlying the establishment and spread of blackleg largely remain unknown.

What will you be doing?

Identify the principle drivers of potato blackleg through GIS analysis of historical soil, climate, and epidemiological datasets, and the design of a focused set of plant assay experiments. Apply advanced machine learning techniques to the data to automatically develop a predictive model (decision support tool) that can guide both growers and government to straightforward disease intervention strategies.

What’s in it for you?

Become an expert in plant disease epidemiology. The unique blend of skills in plant pathology, practical experimentation, theoretical and computational epidemiology that will be gained are highly marketable and much sought after in both research and the agricultural industry. You will receive training in state-of-the-art techniques for analysing epidemiological data, in addition to field and laboratory skills at a world leading research institute. This is a unique opportunity to work with and learn from a highly experienced supervisory team with internationally recognised expertise, and to work closely with industry experts.

Candidate requirements

Applicants should have, or expect to obtain, a first-class or 2.1 (Hons) degree in an appropriate subject, such as biology, agriculture/horticulture, data analytics, GIS. A Master’s degree (or equivalent) would be beneficial. The PhD student will be registered with the Division of Plant Sciences at the University of Dundee, and based at the James Hutton Institute in Dundee. The closing date for the return of applications is 5pm on May 13th 2018, with an expected start date of October 1st 2018.

Funding Notes

Full studentships (UK/EU tuition fees and stipend (£14,777 2018/19 [tax free]) for UK/EU students for 3.5 years.

Let us know you agree to cookies

We use cookies to give you the best online experience. By continuing, we'll assume that you're happy to receive all cookies on this website. To read our privacy policy click here