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Field
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-supervised learning, and few/zero-shot techniques — the student will adapt models to ecological data. Bayesian deep learning and ensemble methods will be explored for trustworthy uncertainty estimation
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
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University of California, San Francisco | San Francisco, California | United States | about 17 hours ago
include international field site management, data management and human factors research. As desired, the project provides opportunities to be involved in Bayesian analytic methods and health economic
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University of California San Francisco | San Francisco, California | United States | about 2 months ago
sources, including developing a Bayesian Hierarchical Modeling framework; (2) using integrative modeling approaches to characterize heterogeneous protein assemblies structures and dynamics; (3) developing
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Neural Networks (PINNs), Neural ODEs, Bayesian learning, and system modeling and simulation Strong background in numerical methods and machine learning Good programming skills, preferably in Python
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, integrating five innovations. First, you will establish material accountancy algorithms using Monte Carlo simulation and Bayesian inference to distinguish normal tritium losses from suspicious discrepancies
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. This robust combination drives substantial advancements in optimization, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in
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maximum of ONE student per project. This process will ensure an excellent fit of student to project and also an excellent strategic fit of the project within the faculty. Project titles: Bayesian methods