Research Associate​/Senior Research Associate in Greenhouse Gas Inverse Modelling

Research Associate/Senior Research Associate in Greenhouse Gas Inverse Modelling

Atmospheric observations of greenhouse gases and ozone depleting substances are playing an increasingly important role in evaluating the success of climate agreements. We are seeking a research associate to develop and use greenhouse gas modelling and data analysis frameworks for national and global emissions evaluation. This work will contribute to several exciting UK-wide and international projects, including:

• TheOpenGHG.org project, a cloud-based community platform for greenhouse gas data analysis

The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks, to estimate surface fluxes from in situ and satellite observations. The derived emissions are used to track progress on climate agreements, improve national inventory reports, help decision-makers effectively target mitigation measures, and learn about natural emissions changes.

The postholder will work with UK, European and other international greenhouse gas monitoring networks, to interpret measurement data using atmospheric models. They will have the opportunity to use and develop atmospheric models of varying complexity and examine regional emissions trends using high-resolution simulations and data. To combine the data and models, and estimate uncertainties, they will develop and use Bayesian ‘inverse modelling’ techniques.

You will work closely with a team of around 10 researchers in the ACRG studying greenhouse gases and ozone depleting substances, with several institutions across the UK and Europe through the InHALE, GEMMA and PARIS projects, and with international colleagues in the AGAGE and NOAA networks.

We are seeking an ambitious, self‑motivated researcher who holds a postgraduate qualification in physical sciences, computing, mathematics, or similar fields. A high level of computational expertise is essential. Familiarity with Bayesian methods and/or atmospheric science is desirable. Excellent communication and team-working skills are essential.

Contract type:
Open ended with fixed funding until 01/02/2028

The intention is to offer the post with an initial term of 1–2 years, which can likely be extended, if mutually agreeable. The post is available to start as soon as possible.

Work pattern:
Full time

Grade: I‑J/Pathway 2

School/Unit:
School of Chemistry

This advert will close at 23:59 UK time on Thursday 8th January 2026.

Interviews are expected to take place on Wednesday 14th January 2026.

For informal queries, please contact:
Prof in the School of Chemistry, Matt Rigby

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