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of Oslo. Job description A fully funded PhD position is available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian
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the team’s work across its different content areas. We are seeking a candidate with strong quantitative and statistical modeling skills, particularly in Bayesian methods, who is ready to advance their career
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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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interest in using computational methods such as cognitive and psychophysiological modeling, (Bayesian) statistics and optimal experimental design, and agent-based modeling to address problems in
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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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, or network-based, Bayesian or matrix factorization methods for multi-omics integration Ability to independently perform data analysis and scientific interpretation based on omics data at an internationally
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. Please direct all questions about the position to Dr. Jessica Jaynes at jjaynes@fullerton.edu . Statistics at CSU Fullerton The statistics faculty research areas include Bayesian statistics, statistical
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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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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