The role
The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model that can estimate atmospheric trace gas source-receptor relationships, or measurement "footprints", orders of magnitude more quickly than traditional 3D simulators (https://doi.org/10.5194/egusphere-2025-2392). We have recently demonstrated its use in a Bayesian framework for evaluating South American methane emissions. In this role, you will continue the development of GATES under two grants from the Natural Environment Research Council (NERC), which aim to:
- Evaluate global methane emissions using GATES and observations from mapping satellites such as GOSAT and TROPOMI
- Apply GATES to higher-resolution retrievals from MethaneSAT
Together with the project PI, you will be expected to lead the development of GATES and related activities, which will include:
- Providing guidance on technical and strategic aspects the project
- Tutoring other team members on the use and development of GATES
- Leading and contributing to funding proposals for ongoing model development
- Developing a sustainable future funding model for GATES and associated products
- Disseminating outputs of the project to key stakeholders and other scientists
Advancing the GATES software to improve model scope and accuracy, and contributing to the development of Bayesian inference frameworks that use GATES.
What will you be doing?
The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling frameworks to estimate emissions of greenhouse gases. They will work closely with a team of around 10 researchers in the ACRG and School of Engineering Mathematics studying greenhouse gases and ozone depleting substances. They will liaise with international colleagues and stakeholders. They will be expected to contribute to the leadership of the GATES model development and related projects and the supervision of other staff members, and they will be expected to lead and contribute to future funding bids.
You should apply if
We are seeking an ambitious, self-motivated researcher who has expertise in machine learning, deep learning and graph neural networks. You should have experience of building machine learning models for environmental applications. A high level of data science and computational expertise is essential, as is experience with Bayesian methods and atmospheric science. Excellent communication, academic writing and teamworking skills are a requirement for the post.
Additional information
For informal queries please contact: Prof. Matt Rigby: matt.rigby@bristol.ac.uk
Contract type: Open ended with fixed funding for 3 years
Work pattern: Full time
Grade: J / K- Pathway 2
Salary: Grade J £43,482 - £50,253 per annum; Grade K £50,253 - £58,225 per annum
School/Unit: Physical and Theoretical Chemistry
This advert will close
at
23:59 UK time
on 28/10/2025
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