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quality, avoiding the paralysis that troubles artificial algorithms when options seem equally good. This project asks: what objective functions do such biological systems optimise, and how can we use
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, less dependent on a strict observation model, and better adapted to both interferences and very low SNRs. Objective – Topic 1: Explore how the statistics and geometry of noise in the time–frequency
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 2 months ago
on models uncertainty. Currently, Breed method uses importance sampling technique and loss statistics. In the beginning, the objective is to get familiar with the domain and read about existing work
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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currently consists of fourteen tenured/tenure-track faculty and nine full-time instructors. Current research areas of the faculty include survival and reliability analysis, Bayesian statistics, latent
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and supervision of postgraduate research students. To develop research objectives and proposals for own or joint research including research funding proposals To attend and or present at conferences
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methods, Bayesian statistics, and/or an interest in applied empirical problems. We are particularly interested in candidates with expertise in applications of artificial intelligence in marketing
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their academic and career goals while advancing the University’s strategic plan objectives. CSUF strives to retain all faculty by providing resources to build meaningful connections and community within and across
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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