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Field
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computers lack such abilities. The goal of the Adaptive Bayesian Intelligence Team is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with
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Research Associates/Assistants to support a diverse portfolio of research projects at the intersection of infectious disease modelling, Bayesian inference, AI, and public health. Projects span AI-driven
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for a Senior Research Fellow to help lead a vibrant, internationally connected research programme spanning Bayesian infectious disease modelling, AI-driven epidemic forecasting, genomic epidemiology, and
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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available
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advanced statistical analyses of large-scale longitudinal panel data, contributing to the development and testing of hierarchical Bayesian computational models, and producing research outputs for publication
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biological properties of LATs, including particle size, degradation rate, and drug release profiles. You will build representational methods for APIs and excipients, apply Bayesian optimisation to experimental
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to characterize developmental patterns from multi-omics data Developing and parameterizing mechanistic mathematical models describing microbiome-immune dynamics Applying Bayesian inference and model fitting
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prompted by the same environmental stressors across a species’ geographic range and through time. The post holder will develop a new Bayesian model, MESS, to analyse the dynamics of extirpation. The MESS
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guidance, navigation, and control (GNC) systems. The successful candidate will develop and validate Bayesian and non-Gaussian estimation algorithms, data assimilation methods, and tracking frameworks
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environmental stressors across a species’ geographic range and through time. The post holder will develop a new Bayesian model, MESS, to analyse the dynamics of extirpation. The MESS model will adapt