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model fitting, including Bayesian model fitting. Experience of management and analysis of large multidimensional real world data sets. What we can offer you The opportunity to continue your career at a
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and Bayesian methods. Knowledge of statistical software, particularly R. Strong statistical programming skills. Understanding of clinical trials. An ability to work well both on own initiative and
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Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted
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, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging
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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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- based neural networks, Bayesian statistics, and text analytics are a must. Nice to Have: Experience developing and integrating APIs for healthcare systems to ensure seamless interaction with AI models
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disease data. A collaborative mindset, excellent communication skills, and the ability to work across disciplines. (Desirable) Familiarity with malaria epidemiology, Bayesian methods, or international
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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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multivariate methods, network analysis techniques, Bayesian methods, power and sample size calculation, statistical methods for genomics and sequence analysis (including next generation sequencing platforms
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required. Knowledge of statistical modelling and Bayesian methods. Knowledge of statistical software, particularly R. Strong statistical programming skills. Understanding of clinical trials. An ability