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have recently developed a new method of statistical analysis for the problem of finding the key genes for disease, with exciting pilot results and promising drug targets for rheumatoid arthritis and
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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presentations, response to therapy, disease progression and complication, and that further subclassification of diabetes into more homogeneous groups offers opportunities for tailored and targeted early treatment
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possible thereafter. The aim of this project is to advance the development of multi-trait Bayesian linear regression models that enable the sharing of genomic information across traits and biological layers
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candidate, you will: Develop and apply Bayesian Network machine learning methods to analyze the dynamics of G-protein coupled receptors to uncover allosteric regulation that enables design of allosteric
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predictions. To mitigate these effects, advanced ML techniques such as Bayesian deep learning, probabilistic models, and uncertainty quantification methods can be applied to enhance model robustness