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to the development of Bayesian inference frameworks that use GATES. The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling frameworks to estimate
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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
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Climate Plan. You will research, use and build on existing methods to take data about the subsurface (seismic surveys, borehole data, geological mapping and other data) and produce estimates of the physical
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, borehole data, geological mapping and other data) and produce estimates of the physical properties of the subsurface, and crucially, the associated uncertainty on those estimates. Initially, you will focus
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vaccination barriers and facilitators, develop forecasts of vaccine coverage for existing and novel vaccines (e.g. HPV, RSV, malaria), and support the design and implementation of small area estimation and
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vaccination barriers and facilitators, develop forecasts of vaccine coverage for existing and novel vaccines (e.g. HPV, RSV, malaria), and support the design and implementation of small area estimation and
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as opportunities allow. We are looking for a post-doctoral researcher to join a research project which brings the Active Inference perspective to the study of human computer interaction. This requires
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the appropriate area. Familiarity with statistical analysis software (e.g., STATA, R, SPSS) or computer programming (e.g. C++, Python, R) and experience working with health-related data will be advantageous