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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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the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
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the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
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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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publication(s) The following qualifications will count in the assessment of the applicants: Familiarity with Bayesian estimation techniques Familiarity with machine learning methods Proficiency in IRT Personal
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probability, partial differential equations, and mathematical physics. In statistics, these include biostatistics, optimal design, computer experiments, sequential analysis, shape-constrained inference, time
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employment. Position description The successful candidate will work within the research project “Advances in generalized Bayesian inference via differential-geometric methods” funded by the Research Council
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: Familiarity with Bayesian estimation techniques Familiarity with machine learning methods Proficiency in IRT Personal skills A collaborative, friendly, and team-oriented style of work Ability to join
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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