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machine learning (ML) fundamentals and extensive experience with deep learning (DL) and large language models (LLMs), a track record of successfully using these methods to answer complex research questions
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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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