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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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. This position is designed for scientists with strong computational and quantitative training who are interested in agent-based modeling, network science, infectious disease dynamics, uncertainty quantification
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in AI for genomics (e.g., generative models, transformers, genomic language models, agentic AI) and related areas of statistics (e.g., uncertainty quantification for machine learning and AI). Apply
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(e.g. model-aided, convergence analyses, universal), data-driven detection and estimation, robust adaptive filtering / beamforming for radar/sonar under modeling uncertainties. In addition to research