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multivariable regression modeling, survival analysis, propensity score methods (matching, weighting, stratification), longitudinal data analysis, hierarchical modeling, and causal inference techniques. Develop
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environments and/or containerization Strong interest or familiarity with transposable element biology Ability to understand Expectation-Maximization algorithms or Bayesian statistical methods Track record
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candidate would be a field-based population/community ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science
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imputation, nonparametric methods, Bayesian framework, and predictive model development and validation. • Implements bioinformatic pipelines and performs data analysis to support omics studies, including
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., nanotechnology, quantum computing, novel circuits and devices for sensing, learning, inference, and computing/communication), Novel Computing Hardware and Systems (e.g., Hardware designs for AI/ML, energy
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health equity or assessing the impact of AI on health equity Strong programming skills in R, Python, Bayesian machine learning, etc. Experience using computing clusters (batch processing), cloud computing