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Field
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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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(for example, R, Python, or Matlab). Experience with graph modeling, Bayesian statistics, or causal inference is a plus. The candidate will join an integrated team of computational scientists, molecular
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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high-dimensional neural data. Approaches used include neural network-based approaches, Bayesian inference, and more Assisting with the oversight of day-to-day functions of the lab and shared lab spaces
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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tenured/tenure-track faculty and nine full-time instructors. Current research areas of the faculty include survival and reliability analysis, Bayesian statistics, latent variable methods, item response
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developing and testing the computational mechanisms of social inference, although will have plenty of scope, and will be encouraged, to develop and expand their own research interests. The postholder will work
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diagnosis of psychosis. The postdoctoral researcher will lead a research program focused on developing and testing the computational mechanisms of social inference, although will have plenty of scope, and
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-level models, Bayesian inference, latent class analysis) Strong data visualization skills using packages such as ggplot2, seaborn, or matplotlib Experience with clinical research databases and data
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, and social sciences scholarship across the school. Examples of topic areas include (but are NOT limited to): models for inference (e.g., SEM/CFA, Bayesian modeling, linear mixed effects), data mining