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on hierarchical Bayesian models that allow us to integrate heterogeneous, but complementary, ecological and environmental data. Depending on the background and interest of the candidate, the work will focus on a
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are seeking a postdoctoral researcher to develop methods for analyzing large scale biodiversity and ecosystem function data. Our approach is based on hierarchical Bayesian models that allow us to integrate
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. Prior exposure to experimental data from photon-counting or time-resolved detectors. Experience with Bayesian methods, uncertainty quantification, or real-time data processing. Familiarity with
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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to experimental data from photon-counting or time-resolved detectors. Experience with Bayesian methods, uncertainty quantification, or real-time data processing. Familiarity with distributed computing or HPC
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individual rates of ageing. Role You will extend BrainAGE from global estimates to regional normative models using Bayesian regression and GAMLSS to derive age- and region-specific reference distributions
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project is to develop scalable and privacy-preserving Bayesian computational algorithms. The position is intended for two to three years, with an initial one-year appointment renewable contingent upon
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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mathematical information science approaches, such as scientific machine learning. Potential research topics include, but are not limited to: (1) Bayesian estimation of 3D velocity structure models using ocean
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of using Bayesian methods in both model development and fitting. Previous experience and knowledge of research methods and study design in clinical trials. Knowledge of Good Clinical Practice (GCP) in