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Field
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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Bayesian risk quantification for accelerated clinical development plans (C4-MPS-Oakley) School of Mathematical and Physical Sciences PhD Research Project Competition Funded Students Worldwide Prof J
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Intensive Care National Audit & Research Centre - ICNARC; | London, England | United Kingdom | about 1 month ago
Clinical Trials Unit (CTU) that is designing and delivering cutting-edge clinical trials employing frequentist and Bayesian methods in adaptive and platform trial designs. We are looking for a Principal
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with researchers at Chalmers and the University of Gothenburg. You will explore how Bayesian methods can enable risk-aware, real-time trajectory planning and contribute to the development of autonomous
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University of Gothenburg. You will explore how Bayesian methods can enable risk-aware, real-time trajectory planning and contribute to the development of autonomous vehicles that are both safe and trustworthy
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Location: South Kensington Campus About the role: We are looking for a motivated Research Associate in Bayesian Optimisation & Experimental Design to work with Professor Ruth Misener and Dr Calvin
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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increasingly important, but also more complex, due to rising demands on performance, precision, quality, and sustainability. Bayesian optimization (BO) - a special machine learning approach - represents a
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Bayesian risk quantification for accelerated clinical development plans (C4-MPS-Oakley)
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing