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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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We are looking for a postdoctoral researcher to develop and implement tools for analysis of output from Bayesian inference under phylogenetic models About the position A postdoctoral researcher
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for Advanced Epidemic Analytics and Predictive Modeling Technology”, which is a part of the first national network for outbreak analytics and disease modeling assembled by the CDC’s Center for Forecasting and
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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project on Bayesian comparisons between artificial and natural representations to improve our understanding how natural and artificial intelligences process information. The project is led by Heiko Schütt
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of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and six centers and institutes that focus
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Expertise in quantitative modeling, computational and/or Bayesian methods Expertise using at least one programming languages in the analysis of scientific data such as R, Python, Matlab, or Julia. Expertise
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization