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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
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statistics, or decision science, who specializes in one of the following areas: statistical learning theory, causal inference, Bayesian statistical modeling, or AI and data management. [Desirable
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, Bayesian analysis, modern causal inference, biomarkers analyses, statistical genetics and genomics, computational statistics, systematic review methodology, structural equations modeling, and muti-omics
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, Bayesian analysis, modern causal inference, biomarkers analyses, statistical genetics and genomics, computational statistics, systematic review methodology, structural equations modeling, and muti-omics
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cancer cohorts. • Proven ability to develop novel computational and AI-driven methodologies, including Bayesian generative models, phylogenetic inference tools, and algorithms for multimodal data
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of Bayesian inference, data science, and machine learning. Experience in scientific computing in C++, Python, and/or Julia. Knowledge of electrochemistry and materials science (desirable). Ability to work as a
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Multiple Research-Intensive Associate/Full Professor Tenure System Positions & an 1855 Professorship
, implementation science, geospatial analysis, biostatistics and research design, analysis of interventions (e.g., difference-in-differences), AI analytics, agent-based modeling, Bayesian modeling, causal inference
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of multi-modal healthcare record data. The ideal candidate will additionally have experience: Multi-modal AI model development Statistical modelling techniques (Bayesian inference, differential equations and
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interdisciplinary collaboration. Faculty and students conduct innovative research in Bayesian methods, causal inference, data science, machine learning, statistical genetics, longitudinal and survival analysis, and