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’ or ‘internationally excellent’. The highly research active SP Section comprises 13 permanent academic staff with research interests in Bayesian computational statistics and machine learning, uncertainty quantification
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virtual reality systems, code language models, high-level synthesis, Bayesian neural networks, and FPGA-based acceleration. To apply as a Research Associate you must have a PhD (or equivalent) in
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of economically valuable species and invasive species that may compete with economically valuable species or promote wildfire. Learning Objectives: The postdoctoral fellow will have the opportunity to apply
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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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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 27 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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Bayesian statistics, statistical computing, spatial statistics, experimental design, and survival analysis. Faculty are active in application areas of neuroscience, geology, biostatistics, psychometrics
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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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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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modern clinical trial design, such as Bayesian Adaptive Clinical trial design or established expertise in statistical methods such as structural equation modeling, causal data analysis. Experience in