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Field
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design of experiments methods, based on Bayesian Optimisation. In addition, the team at Cambridge has its own high-throughput and robotics facilities which we use as a testbed in developing new ML methods
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the admission requirements for a PhD at ETH Zurich Experience in machine learning, optimization, or AI-driven decision-making Preferably with knowledge of Bayesian optimization or Gaussian processes
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Interview Motivated in learning new methodologies and applying new knowledge Essential Interview Knowledge of the approximate Bayesian machine learning (e.g. MCMC) (assessed at: Application form/Interview
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Natural Language Processing, Applied Machine Learning, Neural Networks and Deep Learning as well as Machine Learning for AI and Data Science and Bayesian Theory and Data Analysis. We are looking
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health environment and 0 to 1 year of experience. Strong background in one or more areas of machine learning (Bayesian networks, neural networks, Markov Models, convolutional networks etc.) Exposure
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and reduction Development and application of big data analytics for large X-ray data sets Application of Bayesian methods to X-ray data Combinatorial analysis of various data from complementary
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model. The post holder will take the lead in developing and testing Bayesian joint models for relating height and body composition growth features to early life exposures and later outcomes. They will use
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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing
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-dimensional statistics, semiparametric/nonparametric methods, and Bayesian statistics. The teaching duties will be assigned by the Head of Department with a reduced teaching load in the first year of
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patients by estimating the systemic exposure to the drugs from the population models combined with drug measurements using Maximum a Posteriori (MAP) Bayesian estimations. The work is to lead to several