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/drawbacks. Experience with Bayesian statistics a plus. Experience with censored datasets a plus. Proven record in writing successful research proposals. Demonstrated ability of working in a multidisciplinary
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
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processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian
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regression models to complex forms of observational data, and of applying causal inference approaches to health data are essential. Experience of analysing time-to-event outcomes is desirable. This role offers
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regression models to complex forms of observational data, and of applying causal inference approaches to health data are essential. Experience of analysing time-to-event outcomes is desirable. This role offers
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health challenges through innovative software solutions. Our research focuses on inferring the causal processes that underlie diseases in diverse populations. Our analytical processes have historically
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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approaches. Experience in programming in R, using GitHub, and doing Bayesian statistical analyses with the use of MCMC samplers such as JAGS, STAN, or NIMBLE. Point of Contact Justina Eligibility Requirements
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embryos. Multimodal profiling, in vitro differentiation and in vivo chimaera formation will be employed to evaluate and validate naïve pluripotency. Transcriptomics and network inference will be supported