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SUSTAIN CDT: Harnessing Explainable AI to Identify Key Microbial Drivers of Reduced Ruminant Greenhouse Gas Emissions

School of Biological Sciences | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
SBIO-2024-1237
Application Deadline
27 October 2024
Start Date
1 October 2025

Overview

PLEASE NOTE THAT APPLICATIONS MUST BE SUBMITTED FOLLOWING THE PROCESS DETAILED AT: https://www.sustain-cdt.ai/vision Microbes form stable communities by each taking on unique roles based on their genes, a process known as niche specialization. These communities can significantly impact their hosts, influencing health, efficiency, and environmental emissions such as methane in ruminants. This project aims to leverage explainable AI to analyse metagenomic microbiome data from ruminants, like cows, to understand how specific microbial communities can lead to lower emissions. The primary objectives will include identifying microbial communities associated with high and low emissions and using AI to determine which factors contribute to stable low-emission states.

Research Methodology:

The project will focus on deciphering the complex interactions within microbial communities and their ecological roles. By employing advanced AI techniques, the project will aim to unravel the intricate relationships that govern these communities. The project will benefit from the world-leading expertise in Queen’s University Belfast in microbial genomics, utilising our in-house Tier 2 High Performance Computing (HPC) facility and expertise in explainable AI from our project partners in Strathclyde University.

Training:

The successful candidate will have the opportunity to gain a deep understanding of explainable AI techniques and their applications in analysing microbial communities. They will develop skills in handling large datasets through advanced data science methods and gain expertise in bioinformatics, utilizing computational tools to study microbial genetics. The project will also provide a strong foundation in research methodology, including experiment design and result interpretation. Additionally, the successful candidate will be at the forefront of efforts to reduce the environmental impact of microbial communities on greenhouse gas emissions and sustainability. The interdisciplinary nature of the project will foster collaboration across fields such as microbiology, AI, and environmental science, equipping participants with valuable skills for careers in research, environmental management, and data science.

Funding Information

Full information on funding and eligibility is available from the SUSTAIN CDT website:
https://www.sustain-cdt.ai/vision

Project Summary
Supervisor

Professor Chris Creevey

More Information

askmhls@qub.ac.uk

Research Profile


Mode of Study

Full-time: 4 years


Funding Body
SUSTAIN CDT
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