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learning models such as Bayesian optimization, neural networks, random forests. A high proficiency in spoken and written English. Excellent communication and interpersonal skills. You are expected to learn
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& Partnerships (NSF-TIP) directorate. More information on the project is available at: https://industriesofideas.ai/ . Term-limited: This is a term-limited position for two years, with the possibility of renewal
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. We are interested in candidates with research interests in causal inference or Bayesian methodology, and we also welcome strong applicants from the broader fields of statistics and machine learning
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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Strategy). F3 Experience of interpreting stable isotope data using, for example, statistical modelling approaches such as Bayesian analysis. F4 Experience of ecosystem modelling software, for example Ecopath
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interests, which include scalable Bayesian methods, spatial statistics, and statistical methods for massive or complex data, in general, and high-dimensional data analysis, statistical machine learning
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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree
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designs such as observational study, randomized clinical trial, adaptive randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis Work
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randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis. · Develop as an educator by taking an active teaching role in POCUS and EBM
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or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials, machine learning, mobile health data, real world evidence, survival