PhD Studentship: Development of new machine learning methods and algorithms for the integration of omics and environmental data to quantify the role of the soil microbiome in carbon sequestration

Computer Science

Location:  UK Other

Closing Date:  Friday 16 June 2023

 Reference:  SCI2188

Supervisor: Prof. Sacha Mooney

Secondary Supervisor: Andy Neal (Rothamstead)

Subject Area: Soil Science, Computer Science

Research Title
Development of new machine learning methods and algorithms for the integration of omics and environmental data to quantify the role of the soil microbiome in carbon sequestration

Research 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.

Award Start Date: 02/10/2023

Duration of Award: 48 months

Terms 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 rates

Applicant Qualification Requirements

PhD in relevant science discipline

How to Apply

email to sacha.mooney@nottingham.ac.uk

Closing Date: 16/06/2023