Scientific Background: Post-weaning diarrhoea (PWD) is a critical health concern in piglets, affecting their wellbeing and productivity. Additionally, antimicrobial resistance (AMR) in piglets presents a significant challenge, exacerbated by factors including biological, environmental, and management practices and may relate to the prevalence of AMR in humans. The recent ban on zinc oxide (ZnO) supplementation previously used to prevent PWD due to environmental and AMR issues, underscores the need to explore its impact on PWD aetiology and incidence, and AMR dynamics. This project aims to understand how ZnO affects the gut microbiome and contributes to the development of AMR in piglets, by using novel machine learning and AI approaches.

Research Methodology: The student will use data from a controlled pig trial including longitudinal time-series microbiome datasets and pig health metrics to profile gut microbiota and identify functional changes due to ZnO. They will employ a combination of linear strategies, probability-based Bayesian techniques, and machine learning approaches to evaluate increasingly complex relationships in relation to microbiome and AMR outcomes. They will focus on cutting-edge machine learning techniques and novel Bayesian analyses to fully leverage available datasets and discern complex relationships between ZnO presence or absence and PWD which may be otherwise unreachable.

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Training and Opportunities: The successful student will gain comprehensive training in AI applications, microbiome-AMR research and analysis within the agri-food sector. Skills developed will include advanced AI and machine learning methodologies, statistical modelling and data analytics, phenotypic assays, microbiome sequencing techniques. Collaborating with pig chain actors and agri-food practitioners, the student will co-create solutions to enhance piglet welfare and farm productivity. This project not only aims to advance scientific understanding but also to develop AI-driven strategies to improve piglet health and mitigate AMR, contributing to animal welfare and agricultural sustainability.