Our 2025 Studentships
Apply by Sunday 27th October 2024
We are delighted to announce our fully-funded, four-year PhD studentships in the application of Artificial Intelligence to sustainable agri-food, to start in October 2025.
Explore the projects in more detail below. Apply via our Application page.
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Dr Anna Jurek-Loughrey, Queen’s University Belfast; Dr Yeran Sun, University of Lincoln; plus an Industry Supervisor
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Remote sensing data analysis plays a crucial role in advancing sustainable agriculture, offering valuable insights and aiding informed decision-making. In the intersection of artificial intelligence (AI) and machine learning, remote sensing introduces distinctive challenges. Read more.
Dr Vladimir Stankovic, University of Strathclyde; Dr Marta Dondini, University of Aberdeen; Dr Lina Stankovic, University of Strathclyde; RHD (Scotland) Ltd
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The agri-sector, especially dairy farming, is a major contributor to greenhouse gas (GHG) emissions with 18% of annual worldwide GHG emissions attributed to animal farming. Understanding the variation in GHG emissions among different farming practices helps to identify sustainable approaches that reduce pollution. Read more.
Dr Robert Atkinson, University of Strathclyde; Dr Lan Qie, University of Lincoln; Prof Christos Tachtatzis, University of Strathclyde; Lactalis
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Satellite Earth Observation (EO) Data has emerged as a game-changer in accurately assessing plant biodiversity and ecosystem functions within agricultural landscapes, spanning both grassland and arable terrains. Read more.
Dr Arran Hodgkinson, Queen's University Belfast; Prof Ilias Kyriazakis, Queen’s University Belfast; Dr Mingjun Zhong, University of Aberdeen; Agri-Food and Biosciences Institute
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Machine learning (ML) is an invaluable modern technique whose insights, leveraged against large-scale databases tracking agricultural practices and outcomes, could radically change farming practices, both to the benefit of livestock and farmers, alike. Read more.
Prof Lina Stankovic, University of Strathclyde; Dr Sean Cullen, Queen's University Belfast; Richard Hey, RHD Scotland Ltd
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The project’s aim is to develop AI-driven methodologies to reduce the GHG emissions of dairy farms. The proposed research will design and implement an AI-based energy management system within farms, mitigating waste due to malfunctioning equipment, and enabling waste heat recovery and thermal storage that can be used to heat sheds, water for disinfection of plants etc. Read more.
Prof Georgios Leontidis & Dr Aiden Durrant, University of Aberdeen; Dr Oorbessy Gaju, University of Lincoln; Dr Iain Place, Waterside Pharmaceuticals
The project aims to develop a crop-physiology-informed multimodal machine learning system to automate plant growth forecasting and health monitoring of medicinal cannabis production in controlled environment agriculture systems. Read more.
Dr David McKee, University of Strathclyde; Prof Paulo A. Prodöhl, Queen's University Belfast; Dr Claire Neil, Scottish Environmental Protection Agency
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This project aims to revolutionise water quality monitoring in aquaculture environments by combining optical oceanography and genomics. By integrating remote sensing data with genetic material analysis, it will enhance early warning systems for harmful algal blooms (HABs) and other threats like microjellyfish. This approach will allow for more precise detection and prediction of HAB events, which is crucial for effective aquaculture management. Read more.
Prof Craig Michie, University of Strathclyde; Prof Chris Creevey, Queen’s University Belfast; Dr Mazdak Salavati, SRUC
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The project focuses on using artificial intelligence (AI) in the dairy sector to identify novel traits with strongest degrees of biological connection to Green House Gas (GHG) emissions from animals. Most dairy farms in the UK and around the world sample the milk on a regular basis for fat, protein, and spectral measurements. Read more.
Dr Tassos Koidis, Queen's University Belfast; Prof Georgios Leontidis, University of Aberdeen; Multus Biotechnology
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The modern practices of industrial farming have faced scrutiny due to their adverse effects on health and the environment. As the global population rapidly increases, the quest for sustainable and nutritious alternatives to traditional animal products becomes crucial. Read more.
Prof Chris Creevey, Queen's University Belfast; Dr Robert Atkinson, University of Strathclyde
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The overarching aim of this project is to apply explainable AI to extensive ruminant-associated microbiome data, in order to understand the ecological drivers of stable-state microbial communities linked to low emissions from the host. Read more.
Dr Linda Oyama, Queen's University Belfast; Dr Mingjun Zhong,University of Aberdeen; Dr Sadie Douglas, AB Neo
This project will utilise AI, including Bayesian models, to integrate and analyse data from controlled trials, manaement practices and microbiome sequencing. The goal is to better understand the interplay between dietary interventions, the gut microbiome, piglet weaning diarrhoea (PWD) and antimicrobial resistance (AMR). It aims to develop AI-driven strategies to enhance piglet health and mitigate AMR, collaborating with agri-food practitioners to co-create solutions that improve piglet welfare and farm productivity. Read more.
Dr Roslyn Henry, University of Aberdeen; Prof Georgios Leontidis, University of Aberdeen; Dr Mamatha Thota, University of Lincoln; Prof Jonathan Hillier & Dr Milad Toolabi, Trinity AgTech
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This project aims to develop precise tools for monitoring biodiversity, enhancing sustainability and conservation efforts in agricultural landscapes. The objectives are to 1) develop deep learning methods for high-resolution landscape mapping using Earth Observation data; 2) implement biodiversity models using deep learning to monitor biodiversity changes on farms over time and 3) integrate high-resolution landscape and biodiversity maps into Trinity AgTech's natural capital navigator, 'Sandy'. Read more.
Prof Eric Morgan, Queen's University Belfast; Prof Adam Kleczkowski, University of Strathclyde; Paul Wagstaff, Self-Help Africa; Rob Strey, PEAT GmbH / Plantix
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This project aims to enhance existing systems for monitoring small ruminant health on smallholder farms in Africa by integrating real-time health information with predictive models of parasite transmissions, providing actionable advice on antiparasitic interventions. The project will apply ML to identify the most informative health indicators and the most efficient monitoring strategies. Climate-driven predictions of parasite transmission potential will be incorporated, allowing monitoring and actions to be calibrated to epidemiological risks. The key output will be a smartphone app, providing this capability to farmers and advisors. Additionally, the project will explore aligning the app with comparable risk prediction tools for plant health, helping farmers and others to identify and mitigate multiple threats to food security through simultaneous impacts on crops and animals. Read more.