PhD Studentship: Using machine learning to understand the role of the soil microbiome in carbon sequestration (SCI286)

Job title:

PhD Studentship: Using machine learning to understand the role of the soil microbiome in carbon sequestration (SCI286)

Company

University of Nottingham

Job description

PhD Studentship: Using machine learning to understand the role of the soil microbiome in carbon sequestrationArea
Computer ScienceLocation
UK OtherClosing Date
Monday 07 October 2024Reference
SCI286Supervisor: Hannah CooperSecondary Supervisor: Andy Neal (Rothamsted)Subject Area:
Soil Science, Computer ScienceResearch Title:
Using machine learning to understand the role of the soil microbiome in carbon sequestrationResearch Description:
Managing natural processes is one of the most practical and effective implementable approaches to removing CO2 from the atmosphere. It is imperative to measure carbon sequestered by natural means accurately, to understand process drivers and uncertainties and to accelerate nature-based carbon sequestration. Soil can store or sequester carbon through microbiological activity, providing a nature-based sink for CO2. However, poorly managed soils can release carbon as CO2 or methane (CH4) to the atmosphere – contributing to climate change and reducing soil health and fertility.This project will develop machine learning (ML) platforms to monitor, quantify and reveal the processes underlying soil carbon sequestration. This approach combines measurements of physical, chemical, and biological functional and evolutionary processes. Soil microbiome research focuses on determining which microbial taxa and functions facilitate carbon capture across a range of climatic conditions. There will be an analytical challenge to integrate datasets of different types, scales and modalities. These relate to the processing and integration of soil chemistry, soil structure (tomographic imaging data) and metagenomic profiling of soil microbiome across different environmental conditions and soil textures. The overall aim is to integrate disparate measurements of physical, chemical, and biological processes in soil to develop a generalizable predictive model of carbon sequestration.Keyword Search:
soil science, computer science, machine learning, artificial intelligence, soil carbonAward Start Date: 01/12/2024Duration of Award: 48 monthsTerms and Conditions:
This research studentship is only available to UK citizens and includes payment of tuition fees and a tax-free stipend based on EPSRC ratesApplicant Qualification Requirements:
BSc degree in relevant science disciplineHow to Apply:
email toClosing Date: 07/10/2024

Expected salary

Location

United Kingdom

Job date

Wed, 02 Oct 2024 02:27:48 GMT

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