AI-Enabled Digital Twins for Predictive Farm Biosecurity and Adaptive Disease Risk Management

Summary

While climate change, economic instability and growing food demand present long-term challenges for agriculture, livestock disease outbreaks pose immediate risks with potentially severe consequences for animal welfare, productivity, farm profitability and biosecurity [1].

Digital twins are emerging as a powerful approach to integrate real-time sensor observations, farm records and computational models to represent and predict the evolving state of livestock production systems [2].  Recent research has demonstrated the potential of machine learning and digital twins to support animal health, environmental monitoring and precision livestock management, while new architectures are extending digital twins from individual animals to whole-farm systems [3].

An emerging area of research couples pathogen pressure, metagenomics and environmental disturbance as key drivers of increased risk in livestock production environments [4].  Early detection and prediction of infectious disease within livestock systems can reduce animal morbidity and mortality, reduce antimicrobial use, protect food supply chains and reduce the risk of pathogens entering wider agricultural and human populations.

The successful PhD candidate will work closely with experienced industrial partners (Complement Genomics and Ilimex) to initially develop multimodal machine-learning models that integrate metagenomic, environmental and farm-management data to generate dynamic disease-risk forecasts.

The digital twin will then be tested to simulate, optimise and evaluate biosecurity interventions, building on our existing research on targeted UVC disinfection, as a dynamic response to changing microbial risk.

The project will establish a closed-loop ‘predict – simulate – intervene – learn’ framework, supporting adaptive, evidence-based farm biosecurity contributing to intelligent, resilient and trusted digital twin infrastructure for sustainable agriculture.

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.

• A comprehensive and articulate personal statement demonstrating interest in the subject area
• Experience using research methods or other approaches relevant to the subject domain

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.

• Applicants should hold, or expect to hold a First Class Honours Degree in a relevant subject area
• International applicants should hold, or expect to hold a relevant Masters Degree at a level equivalent to a distinction at Ulster
• Experience or knowledge of research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Track record of peer reviewed publications

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Ilimex
• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

PhD scholarships are available for this project to all applicants worldwide, regardless of residency or domicile, and will cover:

• Tuition fees.
• A maintenance allowance of approximately £23,000 (exact rate is to be confirmed) per annum for three years, subject to satisfactory academic performance.
• A Research Training Support Grant (RTSG) of approximately £900 per annum.

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, funded from any source, including self-financing students, are NOT eligible to apply for an award.

Students in full-time employment are not eligible for an award.

Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.

Due consideration should be given to financing your studies.

The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:

1. Department for the Economy (DfE) PhD Scholarships
2. Vice Chancellor’s Research Scholarships (VCRS)

Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.