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key role in delivering the following objectives: Develop and validate advanced cardiovascular risk prediction models, including multi-outcome and dynamic models tailored to complex, multimorbid
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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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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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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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activities and course delivery materials to support course objectives, advise students, work collaboratively to identify instructors, and perform service to the unit, university, and profession
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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
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- which may also include Bayesian statistics, machine learning, applied statistics and epidemiology - as demonstrated through publications, citations, external invitations and research funding. You will be
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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