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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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devising successful models, techniques and methods (e.g., regression modelling, causal inference, survival analysis, Bayesian approaches, risk factor estimation) Extensive experience and achievement in
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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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of a mentor, the participant will use a range of phylogenetic methods (including Bayesian) to study how interspecies transmission, genomic reassortment, and farm production practices affect the evolution
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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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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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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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, 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