Research Associate in Greenhouse Gas Inverse Modelling

University of Bristol – Physical and Theoretical Chemistry 

Location: Bristol

Salary: £36,333 to £40,745 per annum, Grade I / Pathway 2 

Hours: Full time

Contract Type: Fixed-Term/Contract

Placed On: 3rd May 2023

Closes: 31st May 2023

Job Ref: ACAD106593

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The role

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:

The post-holder will use 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 the observations. The derived emissions are used to track progress on climate agreements, improve national inventory reports, help decisionmakers effectively target mitigation measures, and learn about natural emissions changes.

What will you be doing?

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.

You should apply if

We are seeking an ambitious, self-motivated researcher who holds, or expects to obtain, a PhD 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 teamworking skills are essential.

The contract will initially be for a 2-year period and can likely be extended, if mutually agreeable. The post is available to start as soon as possible.

Additional information

For informal enquiries, contact: Prof. Matt Rigby 

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