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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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bring significant experience with the architecture of LLM systems, including tokenization, transformer layers, vector databases, model inference, fine-tuning strategies (e.g., LoRA, PEFT), and RLHF
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, including epigenome-wide association studies (EWAS) and causal inference methods. Assist data analyst in performing data acquisition, storage, cleaning, and pre-processing for large-scale, longitudinal
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