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Field
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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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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
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platforms, and interactive analysis environments, with direct user-facing support for physicists working at the analysis facility. Statistical inference infrastructure: the candidate is expected to contribute
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on cognitive diagnostic assessments and student learning in introductory STEM courses, with a focus on Bayesian quantitative methods and mixed-methods education research. Position Number 4C1852 Department
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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing
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FieldAstronomyYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in astrophysics or a related field. - Experience in data analysis. - Proficiency in Bayesian statistics and nonparametric
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, claims data, registries, pragmatic clinical studies, and other real-world data sources. Areas of methodological interest may include uncertainty quantification, conformal inference, causal inference
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techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty
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-investigator project focused on economic, health, and social impacts of past volcanic eruptions that produced substantial amounts of stratospheric aerosols. The analysis will be based on causal inference
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networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty quantification in forecasting and