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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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the voluntary adoption of effective bycatch reduction strategies in targeted fisheries. There will also be opportunities to contribute to writing research proposals and mentoring graduate students. Required
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, kernel machines, decision trees and forests, neural networks, boosting and model aggregation, Bayesian inference and model selection, and variational inference. Practical and theoretical understanding
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA