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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 9 days ago
- Population Genetics Course Description: This course introduces students to the genetic variation between and within populations. The topics include evolutionary forces, quantitative genetics, and Bayesian
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, graphical models, and/or Bayesian methodologies for resolving disease heterogeneity, identifying gene-gene networks, or improving integrative genomic prediction models for common complex human diseases, with
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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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, 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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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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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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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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Bayesian system identification in nonlinear engineering dynamics School of Mechanical, Aerospace and Civil Engineering PhD Research Project Directly Funded Students Worldwide Prof Keith Worden
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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