Elsewhere on Ulster
This project is funded by:
This project is part of the NILab Programme.
Milk production is a key indicator of profitability and animal health in dairy farming. It reflects the cow’s physiological status and responds rapidly to changes in health, nutrition, and management.
Accurate milk yield forecasting enables data-driven decisions to optimise herd productivity, enhance animal welfare, improve financial outcomes, and support sustainable practices.
Moreover, milk output is a critical factor in assessing and mitigating the environmental impact of dairy farming, linking animal health, sustainability, and food security within the One Health framework.
In grass-based dairy production system, milk products arise from a complex interplay of biological, environmental, and management factors.
These systems are inherently dynamic, with time-varying relationships and dynamic feedback loops that influence milk production. Understanding these dynamics is essential for accurate predicting, optimised management, and long-term system resilience under changing climatic and economic conditions.
Traditional predictive models, such as regression and standard machine learning, capture correlations but not causal mechanisms—how factors like feed quality, energy density, genetics, or cow–calf dynamics directly influence milk yield.
This limits their usefulness for decision-making under changing environmental or management conditions.
The emerging field of causal AI moves beyond correlation-based prediction towards cause–effect understanding, counterfactual reasoning, and policy simulation.
Applied to dairy production system, it can support farmers and policymakers to make data-driven, interpretable, and actionable decisions that enhance herd health, productivity, financial performance, operational efficiency, and effective and sustainable agricultural management.
This research proposes to integrate animal, dietary and environmental factors, and the cow–calf production dynamic within a dynamic causal AI framework to understand and predict milk production in grass-based dairy production system.
By explicitly capturing time-varying effects and dynamic feedback loop, the study aims to provide insights into how maternal nutrition, calf growth, and management interventions interact to influence milk production, enhancing both biological understanding and farm-level decision-making.
By combining causal discovery with biological and management knowledge, this study will deliver interpretable, actionable tools for optimising feed and animal management in grass-based dairy production system, thereby supporting sustainable and resilient dairy farming.
The research will address key questions including:
RQ1: What are the key causal drivers and dynamic feedback loops influencing milk production in grass-based dairy production system?
RQ2: How do time-varying dietary factors (feed quality, feed intake, energy density) interact with environmental and physiological variables to influence milk production over time?
RQ3: How does the cow–calf production dynamic including calf birth traits and early growth impact maternal milk production?
RQ4: How can causal AI and dynamic modelling be integrated to improve prediction accuracy of milk production while maintaining interpretability?
RQ5: How can counterfactual simulations support decision-making in optimising feeding and nutrition strategies and improving dairy herd management and health?
This studentship will benefit from a multidisciplinary supervision team with expertise on AI, biomedical nutrition science, animal health and animal biology.
The successful candidate will be based at AI Research Centre (Ulster), and will be working with Nutrition Innovation Centre for Food and Health (Ulster) and the Agri-Food and Biosciences Institute (AFBI).
The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. We particularly welcome female applicants,as they are under represented within the school.
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.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
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.
This project is funded by:
This scholarship will cover tuition fees and provide a maintenance allowance of £21,000 (tbc) per annum for four years (subject to satisfactory academic performance). A Research Training Support Grant (RTSG) of approximately £5000 per annum is also available.
To be eligible for these scholarships, applicants must meet the following criteria:
Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.
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.
Submission deadline
Friday 6 March 2026
04:00PM
Interview Date
March 2026
Preferred student start date
14 September 2026
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