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nutrition and health method development in causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records
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an account to save a job. Sign In Register More PhDs from University of Birmingham PhD Studentship: Robust Bayesian Experimental Design and Inference PhD Studentship: Designing Human-AI Teams for
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Multiple Research-Intensive Associate/Full Professor Tenure System Positions & an 1855 Professorship
Position Requirements and Apply to job #1059449 https://careers.msu.edu/jobs/associate-full-professor-tenure-system-flint-michigan-united-states-86556cc5-396d-471e-b100-1e0d63e7d387 Multiple Associate
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as quantile g-computation, Bayesian kernel machine regression, weighted quantile sum regression, or related approaches. 4. Experience with time-to-event analyses of health outcomes. 5. Knowledge of
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build and extend statistical pipelines. An interest in Bayesian inference applied to biology is also important. A background in computational proteomics or LC-MS/MS analysis, especially peptide/protein
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methods, data reduction techniques such as lasso, structural equation modeling, survival analysis and Bayesian inference Oversee the management and sharing of datasets for the CNDS including large
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information science, probability and statistics, or decision science, who specializes in one of the following areas: statistical learning theory, causal inference, Bayesian statistical modeling, or AI
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healthcare record data. The ideal candidate will additionally have experience: Multi-modal AI model development Statistical modelling techniques (Bayesian inference, differential equations and
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simulations, and is designed to conduct Bayesian parameter inference; semi-analytic jet and accretion modelling codes (in C++, with a Python interface) that are being integrated into the open-source Gammapy
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areas include: machine learning foundations, generative modelling, foundation models, cross-domain/-modality learning, explainable AI and mechanistic interpretability, representation learning, Bayesian